AUTOMATIC SEGMENTATION OF DIGITAL IMAGES APPLIED IN CARDIAC MEDICAL IMAGES

INTRODUCTION TO SEGMENTATION

Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels).

The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images.

The result of image segmentation is a set of regions that collectively cover the entire image.

MEDICAL IMAGE SEGMENTATION

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

They are used for assisting doctors in evaluating medical imagery or in recognizing abnormal findings in a medical image.

ABOUT CARDIAC IMAGES

The heart is a myogenic muscular organ found in all animals with a circulatory system (including all vertebrates), that is responsible for pumping blood throughout the blood vessels by repeated, rhythmic contractions.

The term cardiac (as in cardiology) means "related to the heart"

The vertebrate heart is composed of cardiac muscle, which is an involuntary striated muscle tissue found only in this organ, and connective tissue.

OBJECTIVE

Design an Adaptable Segmentation method for different type of Images

Adapt a multi-level threshold method for segmentation

EXISTING METHOD

Manual Threshold Based Image Segmentation The thresholding consist in to identify in an image, a

threshold of intensity in which the object distinguish better of the back of the image, and in most cases, the choice of threshold takes a subjective criterion of a human operator.

The threshold is selected based on Gray level

DRAWBACK OF EXISTING METHOD

Selection of Threshold is difficult Manual threshold will not be suitable for all images

PROPOSED METHOD

Histogram Calculation Histogram Group Quantization (User can chose the

group) Detection of histogram slope Percentage Entropy Calculation Selection of Maximum Entropy Multi-level threshold detection Segmentation based on Multi-level threshold

BLOCK DIAGRAM

ADVANTAGES

Automatic Threshold method It will be suitable for different type of Images