The computer-based process of identifying the boundaries of lung from surrounding thoracic
tissue on computed tomographic (CT) images, which is called segmentation, is a vital first …

▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by
automating or facilitating the delineation of anatomical structures and other regions of …

Convolutional neural networks (CNNs) have shown remarkable results over the last several
years for a wide range of computer vision tasks. A new architecture recently introduced by …

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image
clustering. The proposed algorithm incorporates the local spatial information and gray level …

This revised and updated second edition–now with two new chapters-is the only book to
give a comprehensive overview of computer algorithms for image reconstruction. It covers …

Image Processing, Analysis and Machine Vision represent an exciting part of modern
cognitive and computer science. Following an explosion of inter est during the Seventies …

We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI)
data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity …

Image segmentation is very essential and critical to image processing and pattern
recognition. This survey provides a summary of color image segmentation techniques …

Vascular diseases are among the most important public health problems in developed
countries. Given the size and complexity of modern angiographic acquisitions, segmentation …

Fuzzy c-means clustering (FCM) with spatial constraints (FCM/spl I. bar/S) is an effective
algorithm suitable for image segmentation. Its effectiveness contributes not only to the …