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영어

Medical care requires intensively Image segmentation which a significant tool used to extract origin of interest from the background. Different segmentation techniques are deployed in medical images leading to an essential developments in both diagnosis and detection process. Such development has managed to assist specialists and doctors specified in the medical care system to diagnose the patients accurately. This study will propose a more developed methodology via incorporating cluster representatives and second derivative filter technique on Carcinoma image. The conventional segmentation algorithm is widely deployed in medical care image, in spite of its advantages; being able to produce a complete division of the image, but it contains major drawbacks represented by the over-segmentations and sensitivity to false edges. This study will expose these major problems of the conventional algorithm and fixing these problems via deploying Cluster Representatives; that uses a set of pixels as representatives that represent the clusters and producing advanced results. The other process deployed is a second derivative filter applied to the segmented image resulting of the demolition of unwanted region from the image. Nevertheless, results of this study has proved that the number of partitions in medical care images are much fewer when comparing partitions produced by deploying the traditional algorithm deployed in medical care images. Such result will assist experts to easily diagnose health problems in a more accurate measure.