Abstract
This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.
Highlights
Cluster based image segmentation is a significant and mathematical process in the Magnetic Resonance (MR) image analysis system for deeper investigation, enhancement, tumor predication and pattern identification
This section discovers the computational complexity of Medical Image Pixels Clustering (MIPC) approach has tested over MR image dataset by three different improved unsupervised clustering schemes namely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM)
The MIPC approach is aimed to trace the dissimilar pattern over the gray scale medical image through automatic identification of the distinct number of highly relative clusters in the medical image dataset based on improved unsupervised cluster schemes for deeper investigation and analysis
Summary
Cluster based image segmentation is a significant and mathematical process in the MR image analysis system for deeper investigation, enhancement, tumor predication and pattern identification It is defined as a process of dividing MR image pixels into different numbers of dissimilar sub regions based on pixel intensity similarity [1]. The major problem in the existing clustering schemes such as semi-supervised and unsupervised methods [3] is that to predetermine the appropriate number of clusters in the unstructured MR image pixel set and respectively the clustering quality is based on predetermined number of clusters To overcome these issues, in this paper a new clustering technique called Medical Image Pixels Clustering, it intentions to automatically separate finite number of dissimilar patterns in the MR image based on different improved unsupervised clustering schemes without predetermined knowledge for deeper investigation, enhancement, pattern predication and analysis
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