Abstract Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical
objects of interest for analysis is essential in almost any computer-aided diagnosis system or …

The principal goal of the segmentation process is to partition an image into regions (also
called classes or subsets) that are homogeneous with respect to one or more characteristics …

Human disease identification from the scanned body parts helps medical practitioners make
the right decision in lesser time. Image segmentation plays a vital role in automated …

In this paper, we present a principled Bayesian method for detecting and segmenting
instances of a particular object category within an image, providing a coherent methodology …

A new variational level set approach is proposed for lung nodule segmentation in lung CT
scans. A general lung nodule shape model is proposed using implicit spaces as a signed …

Diabetic retinopathy (DR) is a disease that forms as a complication of diabetes. It is
particularly dangerous since it often goes unnoticed and can lead to blindness if not …

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of
stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would …

This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis
of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD …

To accurately segment pathological and healthy lungs for reliable computer-aided disease
diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous …

In this paper we present a high-throughput system for detecting regions of carcinoma of the
prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise …