Journal articles: 'Skin features'

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Morinet, F. "TLR3 and skin features." Current Research in Translational Medicine 64, no. 4 (October 2016): 167. http://dx.doi.org/10.1016/j.retram.2016.11.001.

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B. Hassan, Manchu, and Nimmy Venu. "A Study on Histopathological Features of Granulomatous Lesions of Skin." Indian Journal of Pathology: Research and Practice 5, no. 3 (2016): 393–96. http://dx.doi.org/10.21088/ijprp.2278.148x.5316.26.

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Ezekwe, Nneamaka, Thy Huynh, and Robert Brodell. "Distinguishing Features: Linear Rashing is Not Always Koebnerization." SKIN The Journal of Cutaneous Medicine 5, no. 1 (January 1, 2021): 71–74. http://dx.doi.org/10.25251/skin.5.1.17.

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Cutaneous sarcoidosis and psoriasis are dermatologic conditions that can have a similar clinical appearance. Psoriasiform sarcoidosis shows thick white or silvery scaling reminiscent of psoriasis and both conditions can be linear. Psoriasis is associated with linear scaled patches that are induced by mechanical trauma (Koebner Phenomenon). Linear lesions in cutaneous sarcoidosis are not a result of koebnerization, rather cutaneous granulomas appear in well-healed scars decades after the trauma within what can be termed an immunocompromised district. This paper will focus on the features that distinguish psoriasis and cutaneous sarcoidosis, since prompt diagnosis leads to appropriately targeted treatment.

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Ezekwe, Nneamaka, Thy Huynh, and Robert Brodell. "Distinguishing Features: Linear Rashing is Not Always Koebnerization." SKIN The Journal of Cutaneous Medicine 5, no. 1 (January 1, 2021): 71–74. http://dx.doi.org/10.25251/skin.5.1.17.

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Cutaneous sarcoidosis and psoriasis are dermatologic conditions that can have a similar clinical appearance. Psoriasiform sarcoidosis shows thick white or silvery scaling reminiscent of psoriasis and both conditions can be linear. Psoriasis is associated with linear scaled patches that are induced by mechanical trauma (Koebner Phenomenon). Linear lesions in cutaneous sarcoidosis are not a result of koebnerization, rather cutaneous granulomas appear in well-healed scars decades after the trauma within what can be termed an immunocompromised district. This paper will focus on the features that distinguish psoriasis and cutaneous sarcoidosis, since prompt diagnosis leads to appropriately targeted treatment.

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Ruiz-Carrascosa, José Carlos, and Ricardo Ruiz-Villaverde. "Sonographic features of normal skin." ACTUALIDAD MEDICA 99, no. 793-Suplemento I (November 24, 2014): 9–12. http://dx.doi.org/10.15568/am.2014.793.sp01.re02.

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LEYDEN, J. J. "Clinical features of ageing skin." British Journal of Dermatology 122, s35 (April 1990): 1–3. http://dx.doi.org/10.1111/j.1365-2133.1990.tb16118.x.

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Kawulok, Michal, Jolanta Kawulok, and Jakub Nalepa. "Spatial-based skin detection using discriminative skin-presence features." Pattern Recognition Letters 41 (May 2014): 3–13. http://dx.doi.org/10.1016/j.patrec.2013.08.028.

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Badon, Hannah Roberts, Joy King, Robert T. Brodell, and Adam Byrd. "Distinguishing Features: Staphylococcal Scalded Skin Syndrome vs Toxic Epidermal Necrolysis." SKIN The Journal of Cutaneous Medicine 2, no. 2 (March 9, 2018): 135–39. http://dx.doi.org/10.25251/skin.2.2.7.

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Staphylococcal scalded skin syndrome (SSSS) and toxic epidermal necrolysis (TEN) are dermatologic conditions that have a similar clinical appearance. Careful attention to clinical features, such as the coloration at the base of the blister, and histopathology are utilized to make an accurate diagnosis. While supportive therapy is required for both conditions, SSSS requires appropriate antibiotics to treat the underlying staphylococcus and TEN requires elimination of an offending drug (usually an antibiotic).

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Sinaga, Anita Sindar. "Texture Features Extraction of Human Leather Ports Based on Histogram." Indonesian Journal of Artificial Intelligence and Data Mining 1, no. 2 (November 15, 2018): 92. http://dx.doi.org/10.24014/ijaidm.v1i2.6084.

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Skin problems general are distinguished on healthy and unhealthy skin. Based on the pores, unhealthy skin: dry, moist or oily skin. Skin problems are identified from the image capture results. Skin image is processed using histogram method which aim to get skin type pattern. The study used 7 images classified by skin type, determined histogram, then extracted with features of average intensity, contrast, slope, energy, entropy and subtlety. Specified skin type reference as a skin test comparator. The histogram-based skin feature feature aims to determine the pattern of pore classification of human skin. The results of the 1, 2, 3 leaf image testing were lean to normal skin (43%), 4, 5, tends to dry skin (29%), 6.7 tend to oily skin (29%). Percentage of feature-based extraction of histogram in image processing reaches 90-95%.

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Jun, Jae-Hyun, Min-Suk Jung, Yong-Suk Jang, Cheol-Woong Ahn, and Sung-Ho Kim. "Harmful Image Detection Method Using Skin and Non-Skin Features." Journal of The Institute of Internet, Broadcasting and Communication 15, no. 4 (August 31, 2015): 55–61. http://dx.doi.org/10.7236/jiibc.2015.15.4.55.

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Ferček, Iva, Liborija Lugović-Mihić, Arjana Tambić-Andrašević, Diana Ćesić, Ana Gverić Grginić, Iva Bešlić, Marinka Mravak-Stipetić, Iva Mihatov-Štefanović, Ana-Marija Buntić, and Rok Čivljak. "Features of the Skin Microbiota in Common Inflammatory Skin Diseases." Life 11, no. 9 (September 14, 2021): 962. http://dx.doi.org/10.3390/life11090962.

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Many relatively common chronic inflammatory skin diseases manifest on the face (seborrheic dermatitis, rosacea, acne, perioral/periorificial dermatitis, periocular dermatitis, etc.), thereby significantly impairing patient appearance and quality of life. Given the yet unexplained pathogenesis and numerous factors involved, these diseases often present therapeutic challenges. The term “microbiome” comprises the totality of microorganisms (microbiota), their genomes, and environmental factors in a particular environment. Changes in human skin microbiota composition and/or functionality are believed to trigger immune dysregulation, and consequently an inflammatory response, thereby playing a potentially significant role in the clinical manifestations and treatment of these diseases. Although cultivation methods have traditionally been used in studies of bacterial microbiome species, a large number of bacterial strains cannot be grown in the laboratory. Since standard culture-dependent methods detect fewer than 1% of all bacterial species, a metagenomic approach could be used to detect bacteria that cannot be cultivated. The skin microbiome exhibits spatial distribution associated with the microenvironment (sebaceous, moist, and dry areas). However, although disturbance of the skin microbiome can lead to a number of pathological conditions and diseases, it is still not clear whether skin diseases result from change in the microbiome or cause such a change. Thus far, the skin microbiome has been studied in atopic dermatitis, seborrheic dermatitis, psoriasis, acne, and rosacea. Studies on the possible association between changes in the microbiome and their association with skin diseases have improved the understanding of disease development, diagnostics, and therapeutics. The identification of the bacterial markers associated with particular inflammatory skin diseases would significantly accelerate the diagnostics and reduce treatment costs. Microbiota research and determination could facilitate the identification of potential causes of skin diseases that cannot be detected by simpler methods, thereby contributing to the design and development of more effective therapies.

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Oshyvalova, Оlena. "Epidemiological features of the skin cancer." ScienceRise 3, no. 3 (20) (March 30, 2016): 31. http://dx.doi.org/10.15587/2313-8416.2016.64835.

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Hanna, Wedad, Dianne Friesen, Clair Bombardier, Dafna Gladman, and Amir Hanna. "Pathologic features of diabetic thick skin." Journal of the American Academy of Dermatology 16, no. 3 (March 1987): 546–53. http://dx.doi.org/10.1016/s0190-9622(87)70072-3.

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Mitteldorf, Christina, and Michael Tronnier. "Histologic features of granulomatous skin diseases." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 14, no. 4 (March 29, 2016): 378–87. http://dx.doi.org/10.1111/ddg.12955.

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Jogan, Matjaz, Benjamin Serbiak, and Laura Higgins. "Human perception of localized skin features." Journal of Vision 19, no. 10 (September 6, 2019): 228a. http://dx.doi.org/10.1167/19.10.228a.

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Karymov, O. N., S. A. Kalashnikova, I. O. Solov'yeva, and L. V. Polyakova. "Histotopographic Features of Facial Skin Structure." Journal of Anatomy and Histopathology 6, no. 1 (2017): 29–32. http://dx.doi.org/10.18499/2225-7357-2017-6-1-29-32.

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Vesentini, S., A. Redaelli, and F. M. Montevecchi. "Skin nanostructural features determine suture biomechanics." IEEE Transactions on Nanobioscience 2, no. 2 (June 2003): 79–88. http://dx.doi.org/10.1109/tnb.2003.813925.

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Nadorov, A. V., and O. V. Bushukina. "Morphological features of the skin cattle." International Journal of Veterinary Medicine, no. 1 (April 29, 2022): 146–52. http://dx.doi.org/10.52419/issn2072-2419.2022.1.146.

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The article discusses the comparative morphological features of the skin of cattle of the Holstein-Frisian breed of the frontal region of the head, the gluteal region of the back, the umbilical region of the abdomen, the region of the pastern of the pelvic limb during puberty of the animal. The purpose of this study was to study the topographical features of localization of structural components of the skin of cattle of the HolsteinFrisian breed. The work was performed at the Department of Morphology, Physiology and Veterinary Pathology of the Agrarian Institute of the Ogarev Moscow State University. The object of the study was the skin of Holstein-Frisian cows bred in farms of the Republic of Mordovia. The study material was the skin of the frontal region of the head, the gluteal region of the back, the umbilical region of the abdomen, the pastern region of the pelvic limb. The processing of the material was carried out in the scientific laboratory of "Histophysiology" of the department. Pieces of skin for research were fixed in 10% neutral formalin. Histological preparations were made according to the standard procedure for light microscopy. Histological preparations were stained with hematoxylin and eosin, according to Van Gieson and Mallory. Morphometric studies were carried out using the Image J program. Statistical data processing was carried out using MS Excel 2007 and Statistica 6.0 application programs. The differences were considered significant at a significance level of less than 0.05 (p<0.05). The presence of pedigree morphological features of the skin of four areas of the body was established. They are characterized by differences in the thickness of the skin and its layers, the depth of the sebaceous, sweat glands and hair roots. The matrix of the papillary and mesh layers of the dermis has topographic features of the architectonics of the fibrous component.

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M, Kalaiyarivu, and N. J. Nalini. "Hand Image Based Skin Disease Identification Using Machine Learning and Deep Learning Algorithms." ECS Transactions 107, no. 1 (April 24, 2022): 17381–94. http://dx.doi.org/10.1149/10701.17381ecst.

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The skin disease is most commonly affected to all peoples. It is a big challenging process in skin image identification because of the combined texture shape with the color variations. This paper presented to identify a skin using texture features (Local Binary Pattern and Gray Level Co-occurance Matrix) and color features with various learning models. The proposed work technique for identification of skin disease has two stages: either extracted feature values and identification. During the first stage, texture features and color feature vector values are taken from the images in a dataset of hand skin database. During the second stage, the extracted feature vectors are trained by the various kinds of Machine Learning models. This hand skin image dataset also applied into Deep Learning model for identification. The performance measures of the system is evaluated using accuracy measure. The Convolutional Neural Network (CNN) model gives accuracy of 87.5% when compared to Machine Learning models.

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Mohammed, Kamel K., Heba M. Afify, and Aboul Ella Hassanien. "ARTIFICIAL INTELLIGENT SYSTEM FOR SKIN DISEASES CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 32, no. 05 (August 3, 2020): 2050036. http://dx.doi.org/10.4015/s1016237220500362.

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In this paper, an artificial intelligent technique is proposed for skin disease detection and classification. The suggested method comprises four stages, including segmentation, extraction of textural features, and classification. The stretch-based enhanced algorithm has been adapted for image enhancement. Then the method of an active contour is used for segmentation to determine the skin lesion in tissue. Textural features are obtained from the segmented skin lesion. As several numbers of the features can affect the classification precision, ideal feature selection is made to exclude features that are less informative and unnecessary. The feature selection is adjusted with a regularized random forest. Finally, the classification algorithms by support vector machine and a back-propagation neural network (BPNN) are implemented. The dataset consists of 400 dermoscopic images in total divided into 200 benign and 200 malignant skin diseases extracted from the dermoscopic images PH2 database. The result of detecting and classifying the dermoscopic images on these images yielded an accuracy of 99.7%, a sensitivity of 99.4%, and a specificity of 100% by BPNN. The experiential results confirmed that the BPNN classifier is best rather than an SVM classifier for skin disease images. This proposed model will be advanced to support the skin image processing techniques that provided a more accurate diagnosis and rapid treatment plan.

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Bhadane, Sapana. "Spectrum of Histopathological and Clinical Features in Psoriasis and Other Papulosquamous Skin Disorders." Indian Journal of Pathology: Research and Practice 8, no. 5 (2019): 650–54. http://dx.doi.org/10.21088/ijprp.2278.148x.8519.20.

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Sari, Yuita Arum, Anggi Gustiningsih Hapsani, Sigit Adinugroho, Lukman Hakim, and Siti Mutrofin. "Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction." International Journal of Artificial Intelligence Research 4, no. 2 (January 5, 2021): 95. http://dx.doi.org/10.29099/ijair.v4i2.165.

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Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

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Taha, Mohammed A., Hanaa M. Ahmed, and Saif O. Husain. "Iris Features Extraction and Recognition based on the Scale Invariant Feature Transform (SIFT)." Webology 19, no. 1 (January 20, 2022): 171–84. http://dx.doi.org/10.14704/web/v19i1/web19013.

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Iris Biometric authentication is considered to be one of the most dependable biometric characteristics for identifying persons. In actuality, iris patterns have invariant, stable, and distinguishing properties for personal identification. Due to its excellent dependability in personal identification, iris recognition has received more attention. Current iris recognition methods give good results especially when NIR and specific capture conditions are used in collaboration with the user. On the other hand, values related to images captured using VW are affected by noise such as blurry images, eye skin, occlusion, and reflection, which negatively affects the overall performance of the recognition systems. In both NIR and visible spectrum iris images, this article presents an effective iris feature extraction strategy based on the scale-invariant feature transform algorithm (SIFT). The proposed method was tested on different databases such as CASIA v1 and ITTD v1, as NIR images, as well as UBIRIS v1 as visible-light color images. The proposed system gave good accuracy rates compared to existing systems, as it gave an accuracy rate of (96.2%) when using CASIA v1 and (96.4%) in ITTD v1, while the system accuracy dropped to (84.0 %) when using UBIRIS v1.

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Arias-Santiago, Salvador, María Sierra Girón-Prieto, and Luisa Castellote-Caballero. "Sonographic features of benign common skin tumors." ACTUALIDAD MEDICA 99, no. 793-Suplemento I (November 24, 2014): 21–26. http://dx.doi.org/10.15568/am.2014.793.sp01.re04.

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Dobák, András, and Ildikó Kákonyi. "The microbiological features of chronic skin wounds." Bőrgyógyászati és Venerológiai Szemle 95, no. 5 (November 5, 2019): 206–10. http://dx.doi.org/10.7188/bvsz.2019.95.5.2.

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Wendelschafer-Crabb, G., W. R. Kennedy, and D. Walk. "Morphological features of nerves in skin biopsies." Journal of the Neurological Sciences 242, no. 1-2 (March 2006): 15–21. http://dx.doi.org/10.1016/j.jns.2005.11.010.

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Lawton, Sandra, and Sheelagh Littlewood. "Vulval skin disease:clinical features, assessment and management." Nursing Standard 20, no. 42 (June 28, 2006): 57–64. http://dx.doi.org/10.7748/ns.20.42.57.s53.

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Smith, LTC Kathleen J., CDR Henry G. Skelton, and COL Peter Angritt. "Histopathologic Features of HIV-Associated Skin Disease." Dermatologic Clinics 9, no. 3 (July 1991): 551–78. http://dx.doi.org/10.1016/s0733-8635(18)30403-0.

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Brown, Christine. "Difficult Skin Cancers: Clinicopathologic Features and Management." Baylor University Medical Center Proceedings 6, no. 2 (April 1993): 9–12. http://dx.doi.org/10.1080/08998280.1993.11929814.

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Nouveau-Richard, S., W. Zhu, Y. H. Li, Y. Z. Zhang, F. Z. Yang, Z. L. Yang, S. Lian, et al. "Oily skin: specific features in Chinese women." Skin Research and Technology 13, no. 1 (February 2007): 43–48. http://dx.doi.org/10.1111/j.1600-0846.2006.00185.x.

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KRUPI�SKI, Robert. "Small-Size Skin Features for Motion Tracking." PRZEGL�D ELEKTROTECHNICZNY 1, no. 2 (February 5, 2015): 46–48. http://dx.doi.org/10.15199/48.2015.02.11.

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Goncharova, Yana, Enas A. S. Attia, Khawla Souid, and Inna V. Vasilenko. "Dermoscopic Features of Facial Pigmented Skin Lesions." ISRN Dermatology 2013 (February 3, 2013): 1–7. http://dx.doi.org/10.1155/2013/546813.

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Four types of facial pigmented skin lesions (FPSLs) constitute diagnostic challenge to dermatologists; early seborrheic keratosis (SK), pigmented actinic keratosis (AK), lentigo maligna (LM), and solar lentigo (SL). A retrospective analysis of dermoscopic images of histopathologically diagnosed clinically-challenging 64 flat FPSLs was conducted to establish the dermoscopic findings corresponding to each of SK, pigmented AK, LM, and SL. Four main dermoscopic features were evaluated: sharp demarcation, pigment pattern, follicular/epidermal pattern, and vascular pattern. In SK, the most specific dermoscopic features are follicular/epidermal pattern (cerebriform pattern; 100% of lesions, milia-like cysts; 50%, and comedo-like openings; 37.50%), and sharp demarcation (54.17%). AK and LM showed a composite characteristic pattern named “strawberry pattern” in 41.18% and 25% of lesions respectively, characterized by a background erythema and red pseudo-network, associated with prominent follicular openings surrounded by a white halo. However, in LM “strawberry pattern” is widely covered by psewdonetwork (87.5%), homogenous structureless pigmentation (75%) and other vascular patterns. In SL, structureless homogenous pigmentation was recognized in all lesions (100%). From the above mentioned data, we developed an algorithm to guide in dermoscopic features of FPSLs.

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Hernández-Aragüés, I., I. Vázquez-Osorio, F. Alfageme, C. Ciudad-Blanco, L. Casas-Férnandez, M. I. Rodríguez-Blanco, and R. Suárez-Fernández. "Skin ultrasound features of Merkel cell carcinoma." Journal of the European Academy of Dermatology and Venereology 31, no. 7 (January 19, 2017): e315-e318. http://dx.doi.org/10.1111/jdv.14102.

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Echeverría-García, B., A. Hernández-Nuñez, and J. Borbujo. "Skin Ultrasound Features of Encapsulated Fat Necrosis." Actas Dermo-Sifiliográficas (English Edition) 107, no. 9 (November 2016): 779. http://dx.doi.org/10.1016/j.adengl.2016.08.005.

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Zaslavsky, D. V., and A. N. Barinova. "SKIN MICROBIOME IN ATOPIC DERMATITIS AND FEATURES OF VARIOUS BACKGROUND SKIN CARE." Medical Council, no. 2 (February 12, 2018): 170–76. http://dx.doi.org/10.21518/2079-701x-2018-2-170-176.

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Okoji, Uchenna K., Nnenna G. Agim, and Candrice R. Heath. "Features of Common Skin Disorders in Pediatric Patients with Skin of Color." Dermatologic Clinics 40, no. 1 (January 2022): 83–93. http://dx.doi.org/10.1016/j.det.2021.09.002.

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Motswaledi, Hendrick M. "Allergic skin conditions - causes, clinical features and treatment." South African Family Practice 60, no. 6 (November 30, 2018): 34–37. http://dx.doi.org/10.4102/safp.v60i6.5015.

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Allergic skin conditions are caused by allergens. When an allergen is responsible for triggering an immune system response, this results in an allergic skin condition. Some of these allergens are physical agents which evoke an immune response by way of contact with the skin and some are food-stuffs and drugs taken systemically.Allergic skin conditions include urticaria and angio-oedema, allergic contact dermatitis, atopic dermatitis, hand dermatitis, photoallergic reactions and phototoxic reactions. These conditions are briefly discussed in this article.

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Odrzywołek, Wiktoria, Anna Deda, Julita Zdrada, Dominika Wcisło-Dziadecka, Aleksandra Lipka-Trawińska, Barbara Błońska-Fajfrowska, and Sławomir Wilczyński. "Assessment of Psoriatic Skin Features Using Non-Invasive Imaging Technique." Processes 10, no. 5 (May 16, 2022): 985. http://dx.doi.org/10.3390/pr10050985.

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Background: Psoriasis is one of the most commonly recognized dermatological diseases, characterized by distinct structural changes, hyperproliferation and inflammation. The aim of the study was quantitative comparisons of psoriatic skin with skin without psoriatic lesions by non-invasive imaging methods. Methods: 71 patients diagnosed with psoriasis vulgaris underwent non-invasive imaging of skin at the site of the psoriatic lesion and at the site without such lesion. Skin density, epidermis thickness and subepidermal low-echogenic band (SLEB) thickness were measured by high-resolution ultrasound (HFU). Blood perfusion was assessed using laser speckle contrast analysis (LASCA) and skin temperature was measured by thermal imaging camera. Hyperspectral camera was used to obtain spectral reflectance profiles in psoriatic lesion and skin without psoriatic changes. Results: The greatest differences in skin density and epidermal thickness between psoriatic and unchanged skin were observed on the forearms. The skin covered with psoriatic plaques was 80% less dense, and the epidermis in this area was 121% thicker. The greatest thickness of SLEB was observed in the knee area (Me = 0.389 mm). Skin with psoriatic lesions is characterized by a higher temperature (Me = 33.6 vs. Me = 31) and blood perfusion than skin without psoriasis (Me = 98.76 vs. Me = 50.65). Skin without psoriasis shows lower reflectance than psoriatic lesion from 623 nm to 1000 nm; below this value, skin without psoriatic lesion shows higher reflectance. Conclusions: Skin density and epidermis thickness, skin blood perfusion, temperature and reflectance can be useful parameters for monitoring the course of psoriasis and its treatment, especially since the examination of psoriatic skin with proposed methods is non-invasive, quantitative and easy to perform in clinical conditions.

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Oo, Khaing Thazin, Dr Moe Mon Myint, and Dr Khin Thuzar Win. "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 962–67. http://dx.doi.org/10.31142/ijtsrd18751.

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Manni, Francesca, Fons van der Sommen, Svitlana Zinger, Caifeng Shan, Ronald Holthuizen, Marco Lai, Gustav Buström, et al. "Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery." Applied Sciences 10, no. 12 (June 12, 2020): 4078. http://dx.doi.org/10.3390/app10124078.

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In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.

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Zhou, Xianchao, Shijian Ding, Deling Wang, Lei Chen, Kaiyan Feng, Tao Huang, Zhandong Li, and Yudong Cai. "Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles." Life 12, no. 4 (April 7, 2022): 550. http://dx.doi.org/10.3390/life12040550.

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Atopic dermatitis and psoriasis are members of a family of inflammatory skin disorders. Cellular immune responses in skin tissues contribute to the development of these diseases. However, their underlying immune mechanisms remain to be fully elucidated. We developed a computational pipeline for analyzing the single-cell RNA-sequencing profiles of the Human Cell Atlas skin dataset to investigate the pathological mechanisms of skin diseases. First, we applied the maximum relevance criterion and the Boruta feature selection method to exclude irrelevant gene features from the single-cell gene expression profiles of inflammatory skin disease samples and healthy controls. The retained gene features were ranked by using the Monte Carlo feature selection method on the basis of their importance, and a feature list was compiled. This list was then introduced into the incremental feature selection method that combined the decision tree and random forest algorithms to extract important cell markers and thus build excellent classifiers and decision rules. These cell markers and their expression patterns have been analyzed and validated in recent studies and are potential therapeutic and diagnostic targets for skin diseases because their expression affects the pathogenesis of inflammatory skin diseases.

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Shlivko, I. L., V. A. Kamensky, M. Y. Kirillin, P. D. Agrba, O. E. Garanina, M. S. Neznakhina, D. O. Ellinsky, A. S. Maksimova, and E. V. Donchenko. "Noninvasive study of structural and functional features of neonatal skin." Journal of Innovative Optical Health Sciences 07, no. 03 (May 2014): 1450006. http://dx.doi.org/10.1142/s1793545814500060.

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In the present paper, we describe the first complex multifocal noninvasive morphological and functional study that enabled us to define specific qualitative and quantitative features of neonatal skin. A complex morphofunctional examination of 10 infants aging from 1 to 28 days was performed by optical coherence tomography (OCT) device with a flexible probe at the wavelength of 920 nm with longitudinal resolution of 20 μm and transverse resolution of 25 μm with simultaneous measurement of skin functional parameters. The OCT images of neonatal thin skin have organized layered structure with four horizontally oriented layers. Thick skin of newborns has no structure typical for adult skin and no clear transition from the papillary to the cellular dermis. Thus, we show for the first time to our knowledge that neonatal thick skin differs structurally and functionally from adult skin. Structurally, it differs by a loose arrangement of stratum corneum squamae and thinner epidermis and papillary layer of dermis. The functional differences are lower transepidermal water loss, localization-dependent humidity, higher erythema level, and lower pigmentation. The principal structural differences between neonatal and adult skin show that skin structure formation is not completed by the moment of birth.

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Glazer, Alex, and Clay Cockerell. "Histopathologic discordance in melanoma can have substantial impacts on patient care." SKIN The Journal of Cutaneous Medicine 3, no. 2 (March 11, 2019): 85–89. http://dx.doi.org/10.25251/skin.3.2.41.

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Recommended guidelines for sentinel lymph node biopsy, follow-up, and surveillance for cutaneous melanoma are based upon clinicopathologic staging. In effect, the accuracy of melanoma staging to estimate metastatic risk is critical to subsequent care, neither under-treating or over-treating the patient based on their tumor. Traditional staging continues to evolve based on additional data regarding clinicopathologic features and clinical outcomes. However, such features are subject to inter-observer variability, which puts a limit on their ability to improve prognostication. Reported discordance rates between initial and subsequent pathology review consistently impact both staging and disease management. Newer molecular techniques, such as gene expression profiling, can be used to help define the biology of the primary melanoma tumor and the best course of action after definitive surgical treatment.

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Ibraheem, Mai Ramadan, Shaker El-Sappagh, Tamer Abuhmed, and Mohammed Elmogy. "Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features." Electronics 9, no. 9 (September 4, 2020): 1443. http://dx.doi.org/10.3390/electronics9091443.

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The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

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McWhorter, John. "Sisters Under the Skin." Journal of Pidgin and Creole Languages 10, no. 2 (January 1, 1995): 289–333. http://dx.doi.org/10.1075/jpcl.10.2.04mcw.

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This paper shows that the Atlantic English-based Creoles share six features which are derivable neither from superstratal, substratal, nor universal influences, and therefore constitute idiosyncratic correspondences. The six features indicate that these Creoles all derive from a single ancestor of expanded structure, in contrast to the dominant polygenetic scenario under which the Atlantic English-based Creoles emerged, in essence, independently of one another in their respective locations. The findings have implications for all conceptions of creole genesis, in arguing for diffusion as a pivotal, rather than marginal factor. The features discussed are copulas da and de, pronoun unu, anterior marker bin, adverbial self and the obligative verb fu.

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Wei, Li-sheng, Quan Gan, and Tao Ji. "Skin Disease Recognition Method Based on Image Color and Texture Features." Computational and Mathematical Methods in Medicine 2018 (August 26, 2018): 1–10. http://dx.doi.org/10.1155/2018/8145713.

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Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

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Ciminello, Monica, Antonio Concilio, Bernardino Galasso, and Francesca Maria Pisano. "Skin–stringer debonding detection using distributed dispersion index features." Structural Health Monitoring 17, no. 5 (February 28, 2018): 1245–54. http://dx.doi.org/10.1177/1475921718758980.

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Statistical approaches have been extensively used to detect structural damage. This article proposes a preliminary investigation on the use of a non-model-based damage identification method, implementing “dispersion feature” indicators, extracted from the differential strain signal. In detail, the method takes advantage of the elaboration of the full-spectrum acquisition, irrespective of the healthy state of the structure under test. Large variations of some selected statistical features, representative of the acquired signals, are taken as indicators of the presence of anomalous structural condition. At this stage, these features have been arbitrarily selected. In synthesis, a cumulative dispersion index is defined as the combination of the selected feature variations, contemporarily overcoming prescribed thresholds, providing a scalar output that can be used as an index to identify and possibly quantify the extension of a damage location. Experimental results confirm the envisaged potentiality of the proposed technique. In this study, damage in the form of a stringer debonding is referred to, obtained as a consequence of point impacts on a wing box stiffened panel. A distributed fiber optic strain sensor network is used to detect the input data.

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Markelova, E. M. "Features of management of patients with seborrheic dermatitis." Medical alphabet, no. 9 (June 25, 2021): 29–32. http://dx.doi.org/10.33667/2078-5631-2021-9-29-32.

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Seborrheic dermatitis (SD) is a chronic recurrent inflammatory skin disease caused by changes of quantitative and qualitative sebum characteristics. SD comes out by appearing of erythematous plaques with sense of itch and peeling skin in areas with high concentration of sebaceous glands: on the scalp, face, upper part of the body and in the folds of skin. Due to the fact that the development of the SD is promoted by the colonization of the skin with a lipophilic yeast fungus Malassezia spp., in the treatment of uncomplicated simple forms of the disease can be applied external antifungal medicines, topical glucocorticosteroids and anti-inflammatory drugs of non-steroidal origin. Systemic therapy is also used in severe course of seborrheic dermatitis and in the treatment of forms, resistant to external therapy. In the treatment of persistent and long-term forms of SD, systemic antifungal agents and systemic retinoids are used, which allow for a long time to achieve regression of skin rashes and significantly improve the quality of life of the patient.

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Yadav, Vikash, and Vandana Dixit Kaushik. "Detection of melanoma skin disease by extracting high level features for skin lesions." International Journal of Advanced Intelligence Paradigms 11, no. 3/4 (2018): 397. http://dx.doi.org/10.1504/ijaip.2018.095493.

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YADAV, VIKASH, and VANDANA DIXIT KAUSHIK. "Detection of Melanoma Skin Disease by Extracting High Level Features for Skin Lesions." International Journal of Advanced Intelligence Paradigms 11, no. 3/4 (2018): 1. http://dx.doi.org/10.1504/ijaip.2018.10012484.

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Journal articles on the topic 'Skin features'

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