Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality

Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation.

Optimization-based segmentation approaches deriving from discrete graph-cuts and continuous max-flow have become increasingly nuanced, allowing for topological and geometric constraints on the resulting segmentation while retaining global optimality. However, these two considerations, topological and geometric, have yet to be combined in a unified manner. The concept of shape complexes, which combine geodesic star convexity with extendable continuous max-flow solvers, is presented. These shape complexes allow more complicated shapes to be created through the use of multiple labels and super-labels, with geodesic star convexity governed by a topological ordering. These problems can be optimized using extendable continuous max-flow solvers. Previous approaches required computationally expensive coordinate system warping, which are ill-defined and ambiguous in the general case. These shape complexes are demonstrated in a set of synthetic images as well as vessel segmentation in ultrasound, valve segmentation in ultrasound, and atrial wall segmentation from contrast-enhanced CT. Shape complexes represent an extendable tool alongside other continuous max-flow methods that may be suitable for a wide range of medical image segmentation problems

Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality

Different Exudates Segmentation Techniques in Fundus Images of Diabetic Retinopathy

Now a day’s Diabetic retinopathy is a serious medical issue that mainly harm the human retina and finely vision blindness. The analysis of the Retinal images is done through different diagnosis methods in modern Ophthalmology. There are different methods available for segmentation of the exudates in the fundus retinal images. These methods are used for non-intrusive diagnosis for the eye diseases. Exudates are the manifestations of DR. This paper has demonstrated different methods of exudates segmentation with its advantages and constraints. Accordingly in this paper overview the various main elements of the retina. All examined systems have enhanced the execution in terms of accuracy, specificity and sensitivity. The examination has demonstrated that ant colony optimization based segmentation has better outcomes over each systems

Ant Colony Optimization Based Exudates Segmentation of Fundus Images

Now a days, Diabetic Retinopathy is a deadly form of disease. Diabetic retinopathy is a complication of diabetes and a leading cause of blindness. It occurs when diabetes damages the tiny blood vessels inside the retina, the light-sensitive tissue at the back of the eye. Exudates of diabetic retinopathy appears as white or yellow in color. Early detection of diabetic retinopathy is not possible as patients are generally asymptomatic.  Exudates are frequently observed with microaneurysms. These methods are noise presence, low contrast, uneven illumination, and color variation. Therefore, in order to overcome the above stated issues computer aided diagnosis for exudates segmentation is needed. This proposed system first preprocesses the fundus image of human retina which is followed by image segmentation in which exudates are segmented. Proposed study segments the exudates using Ant Colony optimization Algorithm. The algorithm’s performance was evaluated with a dataset available online. Classification is performed on segmented image to classifying the image as Normal retina and diabetic retinopathy retina

Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors

International audienceBreast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level de-scriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art