The computed density measure is used to classify the region or group of pixels towards mammogram. The density measure shows that the region of the image in which has more impact towards cancer cells.
It computes the density measures for each distinct graph where the connected nodes have similarly valued pixels. The proposed approach enhances the image using histogram equalization technique.The pixel intensity values are adjusted in such a way to increase the contrast of the image that is used to construct the graph.Each pixel is considered as a node and connected with an edge with other neighboring pixel based on the intensity values. A new graph-based density estimation approach will propose to identify the classification of mammograms. The proposed system has three different steps namely, image enhancement, Mammographic Graph Construction, Density Estimation. The earlier approaches suffer in identifying microcalcifications. But the mammogram classification is performed at an initial level of cancer identification. The x-rays are low quality different ultrasonic and other images. The efficiency of classification suffers due to the nature of mammogram images because the x-ray images are low in quality and have taken from low strength rays. The classifying mammograms from low scale X-Ray images have studied in different categories of medical fields. Graph-Based Density Evaluation for Mammographic Image Classification Based On Intensity Values
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