Academic literature on the topic 'Shape-constrained active contours'

We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.

As Many CADx systems have been developed to detect lung cancer based on spatial domain features that process only the pixel intensity values, the proposed scheme applies frequency transform to the lung images to extract frequency domain features and they are combined with spatial features so that the features that are not revealed in spatial domain will be extracted and the classification performance can be tuned up. The proposed CADx comprises of four stages. In the first stage, lung region is segmented using Convexity based active contour segmentation. At second stage ROIs are extracted using spatially constrained KFCM clustering. Followed by standard wavelet transforms is applied on ROI so that transform domain features are extracted with shape and haralick histogram features. Finally neural network is trained by combined feature set to identify the cancerous nodules. Our proposed scheme has shown sensitivity of 95% and specificity of 96%.

The detection, segmentation, and delineation of the targeted regions of interest in biomedical images are fundamental steps for computer-aided assessment and prescreening. In this thesis, we focus on shape-constrained active contours explicitly due to their shape-specificity, efficiency and low computational cost. In particular, they need fewer degrees-of-freedom than other active contour approaches. They are simpler to formulate and optimize. Variants of concentric shape-constrained active contour representations include circular active disc, elliptical active disc, and active oblong. The degrees of freedom include scaling, translation and rotation. The energy is specified using a contrast function and the optimization is carried out using gradient-descent/ascent technique or its faster variants; use Green’s theorem to make the optimization computationally efficient. The thesis addresses segmentation problems for two image classes: retinal fundus images in ophthalmology and ultrasound images of the carotid artery. In retinal fundus image analysis, we consider optic disc, optic cup segmentation, glaucoma diagnosis, creation of a glaucoma specific database, fovea segmentation, oxygen saturation measurement from dual-wavelength oximetry images. In the carotid artery segmentation problem, we consider both transverse-mode and longitudinal model images and develop methods for segmenting the lumen intima and the media adventitia boundary. To start with, we present a reliable and fully automated method for the segmentation and outlining of the optic disc and cup using retinal fundus images with relevant parameters for glaucoma prescreening. We calculate the cup-to-disc ratio (CDR) and rim-to-disc ratio (RDR) from the segmented optic disc and cup. We perform two-stage glaucoma classification using CDR following the International Classification of Diseases (ICD) rules and RDR value following the new disc-damage-likelihood-scale (DDLS) rule. We categorize the glaucomatous condition as normal, moderate, or severe. In addition, we incorporate a check for the pattern of decreasing rim-widths of inferior, superior, nasal, and temporal (ISNT) regions to differentiate normal fundus from a glaucomatous one. We have validated our glaucoma prescreening technique on publicly available as well as locally obtained fundus image databases. The number of retinal fundus images used for the validation of optic disc and cup segmentation is 1597 and 436, respectively, and is 436 for glaucoma severity grading. The algorithm performance is validated against expert clinician outlining and quantitative comparison is provided using Jaccard and Dice similarity measures. The tool is Java/Android/iOS-based, repeatable, easy to use, provides quantitative analysis, and takes only a few seconds per image for the diagnosis. Our solution is available as a smartphone app, “NAYANA,” which assesses severity of glaucoma by computing CDR, RDR, and ISNT parameters. The app is available in both Android and iOS versions. We have tested these versions in a hospital setup with the aforementioned set of algorithm parameters. The results showed that the system could diagnose the severity of glaucoma reliably. As part of this dissertation, we have created a new, comprehensive, and the largest glaucoma-specific retinal fundus images database (with 1500 images) containing images of both glaucomatous and normal eyes. We provide optic disc and cup manual segmentation ground-truth and a decision on glaucoma by five expert ophthalmologists. The database has been created with the help of research funding from Govt. of India’s IMPacting Research, InnovatioN and Technology (IMPRINT) - India initiative. It is a result of collaboration with five expert ophthalmologists to support comparative studies on automatic OD and OC segmentation algorithms using retinal fundus images and subsequent decision on glaucoma diagnosis. We present an automated technique for the measurement of oxygen saturation in retinal arterioles and venules using dual-wavelength retinal oximetry images. The technique is based on segmenting an optic disc centered ring-shaped region of interest and subsequent analysis of the oxygen saturation levels. The two dominant peaks in the histogram of the oxygen saturation levels correspond to arteriolar and venular oxygen saturations from which the arterio-venous saturation difference (AVSD) can be calculated. For evaluation, we have created a normative database of Asian Indian eyes containing forty four dual- wavelength retinal oximetry images. Validations against expert manual annotations exhibit high consistency across the dataset indicating that the automated technique is an accurate alternative to the manual procedure. Proceeding further, we also address the problem of fovea segmentation and develop a technique for delineation of macular regions based on the active disc formalism. We report validation results on three publicly available fundus image databases, amounting to a total of 1370 fundus images for automatic fovea localization and 370 fundus images for fovea segmentation and macular region delineation. With regard to the carotid artery segmentation problem, we develop an automated outlining technique based on circular active disc for outlining the media adventitia boundary and elliptical active disc for outlining the lumen intima boundary of the common carotid artery. We report results of experimental validation on Brno University’s Signal Processing (SP) lab database, which contains 971 transverse mode ultrasound images of the carotid artery. The database provides manual annotations, which are also circular and serve as the ground-truth/reference. The segmentation of the lumen intima layer has been accomplished with the use of elliptical active disc and the performance has been validated on the SP lab database. Comparisons with other state-of-the-art techniques are also reported. We then consider the longitudinal-mode ultrasound images of the common carotid artery and address the segmentation of the lumen intima layer. The method is hybrid in the sense that a coarse segmentation is first achieved by optimizing a locally defined contrast function of an active oblong with five degrees-of-freedom. Subsequently, fine segmentation and delineation of the carotid artery are achieved by post-processing the portion of the ultrasound image spanned by the annulus region of the optimally fitted active oblong followed by a cubic curve-fitting technique to delineate the lumen intima boundary. The algorithm has been validated on 84 common carotid artery longitudinal-mode ultrasound images provided by the SP lab, Brno university. The segmentation results of the proposed technique exhibit a good correlation with the ground-truth annotations provided by two expert radiologists. Finally, we develop a generalized formulation to derive circle/ellipse/rectangle and other shapes using active lp-balls. The concentric-shape strategy, energy function and optimization techniques follow the active disc model. By using one more parameter than required for a rectangle or an ellipse, we are able to significantly increase the degree of flexibility and cover a parametric family of shapes including rectangles and ellipses. We have demonstrated the suitability of active lp-balls for shape-specific segmentation using synthetic images. Applicability of active lp-balls for suitable biomedical applications remains to be explored. In summary, this thesis focuses on various aspects of shape-constrained active contours and demonstrates concrete applications to retinal fundus image segmentation, retinal oximetry assessment, and common carotid artery segmentation in transverse and longitudinal mode ultrasound images.
MAHE, AICTE

An algorithm for lip contour extraction is presented in this chapter. A colour video sequence of a speaker’s face is acquired under natural lighting conditions without any particular set-up, make-up, or markers. The first step is to perform a logarithmic colour transform from RGB to HI colour space. Next, a segmentation algorithm extracts the lip area by combining motion with red hue information into a spatio-temporal neighbourhood. The lip’s region of interest, semantic information, and relevant boundaries points are then automatically extracted. A good estimate of mouth corners sets active contour initialisation close to the boundaries to extract. Finally, a set of adapted active contours use an open form with curvature discontinuities along the mouth corners for the outer lip contours, a line-type open active contour when the mouth is closed, and closed active contours with lip shape constrained pressure balloon forces when the mouth is open. They are initialised with the results of the pre-processing stage. An accurate lip shape with inner and outer borders is then obtained with reliable quality results for various speakers under different acquisition conditions.