The detection of scars in the cerebral cortex usually involves a manual process performed by radiologists, who have to face multiple troubles. For example, the bad calibration of the equipment used to get images of the cerebral cortex can cause spacial and geometrical distortions in the MRI (Magnetic Resonance Imaging) sequences. Owing to the advances in algorithms capable of analyzing MRI sequ...

In this paper, a novel method is presented for unsupervised image segmentation based on local homogeneity analysis. First, a criterion for homogeneity of a certain pattern is proposed. Applying the criterion to local windows in the original image results in the “H-image”. The high and low values of the H-image correspond to possible region boundaries and region interiors respectively. Then, a r...

Color segmentation is crucial in robotic applications, such as RoboCup, where the relevant objects can be distinguished by their color. In these applications, real-time performance and robustness are primary concerns. We present a hybrid method for color segmentation based on seeded region growing (SRG) in which the initial seeds are provided by a conservative threshold color segmentation. The ...

Marginal noise is a common phenomenon in document analysis which results from the scanning of thick documents or skew documents. It usually appears in the front of a large and dark region around the margin of document images. Marginal noise might cover meaningful document objects, such as text, graphics and forms. The overlapping of marginal noise with meaningful objects makes it di5cult to per...

Multimodal segmentation, like the physicians in practice, uses several images, representing different features of the object under investigation, to make segmentation decisions. In this paper, a multimodal twodimensional segmentation method is introduced. The principle of this method is the combination of multimodal region growing and multimodal edge detection. The combining condition is univer...

An automatic method for segmentation of skin cancer images is presented. Firstly the algorithm automatically determines the compounding colors of the lesion, and builds a number of distance images equal to the number of main colors of the lesion (reference colors). These images represent the similarity between reference colors and the other colors present in the image and they are built computi...

This paper describes a semi-automatic method for moving object segmentation and tracking. This method is suitable when a few objects have to be tracked, while the camera moves and fixates on them. The user delineates approximately the initial locations in a selected frame and specifies the depth ordering of the objects to be tracked. First, motion-based segmentation is obtained through an initi...

In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROIs) extracted from the preprocessed image. Second, the initial seeds are automatically selected based on ROIs extracted from the image. Third, the most reprehensive seeds are selected using a machine learning algorithm. Finally, the cellular image is segment...

This paper proposes an effective framework to compute the visibility guarding and star decomposition of 3D solid shapes. We propose a progressive integer linear programming algorithm to solve the guarding points that can visibility cover the entire shape; we also develop a constrained region growing scheme seeded on these guarding points to get the star decomposition. We demonstrate this guardi...

The most widely followed procedure for diagnosis and prognosis of dementia is structural neuroimaging of hippocampus by means of MR. Hippocampus segmentation is of wide interest as it enables quantitative assessment of the structure. In this paper, we propose an algorithm for hippocampus segmentation that is unsupervised and image driven. It is based on a hybrid approach which combines a coarse...