background: brain tissue segmentation for delineation of 3d anatomical structures from magnetic resonance (mr) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (gm), white matter (wm) and cerebrospinal fluid (csf), but only if the obtained segmentation results are correct. due to image arti...

introduction: appropriate definition of the distance measure between diffusion tensors has a deep impact on diffusion tensor image (dti) segmentation results. the geodesic metric is the best distance measure since it yields high-quality segmentation results. however, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. the main goal of this ...

distribution of retinal blood vessels (rbvs) in retinal images has an important role in the prevention, diagnosis, monitoring and treatment of diseases, such as diabetes, high blood pressure, or heart disease. therefore, detection of the exact location of rbvs is very important for ophthalmologists. one of the frequently used techniques for extraction of these vessels is region growing-based se...

background: laparoscopy or minimally invasive surgery is a surgical procedure in which laparoscope and other surgical instruments are inserted inside body via a few small incisions. laparoscope is used to look inside the patient's body and records displayed images. temporal segmentation of laparoscopic videos has many applications like detecting laparoscopic anomalies and interrupts. it is prer...

This paper presents a straightforward top-down segmentation method based on a contour approach on histological images. Our approach relies on a digital deformable model whose internal energy is based on the minimum length polygon and that uses a greedy algorithm to minimise its energy. Experiments on real histological images of breast cancer yields results as good as that of classical active co...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

detecting blood vessels is an important task in retinal image analysis.  the task is more challenging with the presence of bright and dark lesions in retinal images. here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. first, the negative impact of bright lesions is reduced by using k-means segmentation in a perceptive sp...

introduction two-dimensional gel electrophoresis (2dge) is a powerful technique in proteomics for protein separation. in this technique, spot segmentation is an essential stage, which can be challenging due to problems such as overlapping spots, streaks, artifacts and noise. watershed transform is one of the common methods for image segmentation. nevertheless, in 2dge image segmentation, the no...

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...