introduction this research addresses the automatic extraction of alluvial fans using four methods of segmentation from satellite data. this segmentation method divides images into partitions. it is typically used to recognize objects or other relevant purposes in digital images (fu, 2013:3260). alluvial fans have always been a landform that attracts human because they are suitable areas for liv...

breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...

In automatic image interpretation, the process of extracting different objects that compose an image is one of the primary steps. This process is known as image segmentation and consists of subdividing an image into meaningful regions, also called segments, which will be classified in a later step. Many of the existing segmentation algorithms, however, have high computational cost for large ima...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

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...