Probabilistic-based Algorithm for Liver Images Segmentation

Abstract

Medical images processing is the technique or process of creating visual representations of the interior of a body for clinical analysis. Medical images processing is increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression.

Medical imaging seeks to show internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Although imaging of removed organs and tissues can be performed for medical reasons, such procedures are usually considered part of pathology instead of medical imaging.

Medical images processing could be dividing into some stages as pre-processing, segmentation, analysis and diagnosis. In pre-processing stage, kind of filtering or registration technique is done. In segmentation stage, exactly segment objects when it detected (vessels, liver, lung, spine, brain, kidney ...etc.). In analysis stage, make some measurements on segmented objects such as (volume, vessel stenosis, perimeter). In diagnosis stage, classify the output (cancer, not cancer, lesion, not lesion).

Image Segmentation is the process of dividing an image into regions with similar features such as gray level, color, texture, brightness, intensity, and contrast. The role of medical image segmentation is to: study anatomical structure, identify Region of Interest (ROI), i.e. locate tumor, lesion and other abnormalities, measure tissue volume to measure growth of tumor (also decrease in size of tumor with treatment), and also, help in treatment planning prior to radiation therapy; in radiation dose calculation.

Medical Image segmentation is one of the most interesting and challenging problems in computer vision and medical image applications. Medical decisions are rarely taken without the use of imaging technology such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound Imaging (US). Liver segmentation from abdominal computed tomography (CT) images is the essential step in many clinical applications.