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Medical Image Segmentation

methods structures proposed specific

Definition: Medical image segmentation refers to the segmentation of known anatomic structures from medical images.

Structures of interest include organs or parts thereof, such as cardiac ventricles or kidneys, abnormalities such as tumors and cysts, as well as other structures such as bones, vessels, brain structures etc. The overall objective of such methods is referred to as computer-aided diagnosis; in other words, they are used for assisting doctors in evaluating medical imagery or in recognizing abnormal findings in a medical image.

In contrast to generic segmentation methods, methods used for medical image segmentation are often application-specific; as such, they can make use of prior knowledge for the particular objects of interest and other expected or possible structures in the image. This has led to the development of a wide range of segmentation methods addressing specific problems in medical applications.

Some methods proposed in the literature are extensions of methods originally proposed for generic image segmentation. In , a modification of the watershed transform is proposed for knee cartilage and gray matter/white matter segmentation in magnetic resonance images (MRI). This introduces prior information in the watershed method via the use of a previous probability calculation for the classes present in the image and via the combination of the watershed transform with atlas registration for the automatic generation of markers.

Other methods are more application specific; for example in, segmentation tools are developed for use in the study of the function of the brain, i.e. for the classification of brain areas as activating, deactivating, or not activating, using functional magnetic resonance imaging (FMRI) data. The method of performs segmentation based on intensity histogram information, augmented with adaptive spatial regularization using Markov random fields. The latter contributes to improved segmentation as compared to non-spatial mixture models, while not requiring the heuristic fine-tuning that is necessary for non-adaptive spatial regularization previously proposed.

Another important application of segmentation tools is in the study of the function of the heart. In, a contour detection algorithm based on a radial edge-detection filter is developed for cardiac echographic images. Objective of this algorithm is to define a region of interest in which measurements (e.g. image intensity) can lead, after appropriate interpretation, to the estimation of important cardiovascular parameters without the need for invasive techniques.

In addition to the aforementioned techniques, numerous other algorithms for applications of segmentation to specialized medical imagery interpretation exist.

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about 4 years ago

hi sir ,I'm doing project in image processing ,segmentation and classification of liver diseases..will able to help me for MATLAB coding

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almost 7 years ago

sir,am currently doing my project in medical image processing.can u pls tell me how can i segment optical disc from fundus eye image for detecting diabetic Retinopathy?

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almost 7 years ago

Hi i am an engineering student. the material what you published is not in detail. I request you to post the detailed matter about the segmentation. Please post a basic explanation about this in my mail.
Thank You