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Graph Based User Interacted Medical Image Segmentation with Multi Threshold Using K-means Clustering


Affiliations
1 Department of Computer Science, PSG College of Arts and Science Coimbatore, Tamil Nadu., India
2 Department of MCA, St. Xavier’s College (Autonomous) Palaymkottai 627007, Tamil Nadu., India
     

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A combination of Graph cut technique efficiently used to solve a wide variety of low-level computer vision problems, such as image smoothing and k-means clustering with multi threshold for medical image segmentation is presented. The main objective of medical image segmentation is to extract the anatomic structures and its characteristics with respect to some input features. There exist various methodologies for medical image segmentation but struggles with missing features due to the noise presence in the medical images. In propose a new technique to increase the resolution of the medical images to identify the features and edges of the medical images. The medical image is preprocessed to reduce the noise, and then multi level histogram is generated in the first phase of the process. In the second stage, with the initial segmentation obtained with gray level contours, and generate the histogram then the construct the pixel adjacency graph in which each nodes represents the set of pixels of the image and edges links the neighbor pixels. A calculate the similarity of neighboring pixels; based on the distance value, cluster the similar pixels to a class. In use k-means clustering to group similar pixels and used Euclidean distance measure to calculate the similarity between pixels. The proposed method is carried out iteratively until the user gets satisfied. The method will be carried out repeatedly with the user defined threshold value. The threshold is used to group pixels with in the distance. With the proposed technique the features are maintained and the resolution of the image enhanced. The time complexity of the process is reduced. Index Terms:K-means clustering, User Interacted Medical image Segmentation, Threshold based segmentation, Weight Map, histogram, edge detection.

Keywords

K-means Clustering, User Interacted Medical Image Segmentation, Threshold Based Segmentation, Weight Map, Histogram, Edge Detection
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  • Graph Based User Interacted Medical Image Segmentation with Multi Threshold Using K-means Clustering

Abstract Views: 444  |  PDF Views: 2

Authors

T. Karthikeyan
Department of Computer Science, PSG College of Arts and Science Coimbatore, Tamil Nadu., India
S. Chidambaranathan
Department of MCA, St. Xavier’s College (Autonomous) Palaymkottai 627007, Tamil Nadu., India

Abstract


A combination of Graph cut technique efficiently used to solve a wide variety of low-level computer vision problems, such as image smoothing and k-means clustering with multi threshold for medical image segmentation is presented. The main objective of medical image segmentation is to extract the anatomic structures and its characteristics with respect to some input features. There exist various methodologies for medical image segmentation but struggles with missing features due to the noise presence in the medical images. In propose a new technique to increase the resolution of the medical images to identify the features and edges of the medical images. The medical image is preprocessed to reduce the noise, and then multi level histogram is generated in the first phase of the process. In the second stage, with the initial segmentation obtained with gray level contours, and generate the histogram then the construct the pixel adjacency graph in which each nodes represents the set of pixels of the image and edges links the neighbor pixels. A calculate the similarity of neighboring pixels; based on the distance value, cluster the similar pixels to a class. In use k-means clustering to group similar pixels and used Euclidean distance measure to calculate the similarity between pixels. The proposed method is carried out iteratively until the user gets satisfied. The method will be carried out repeatedly with the user defined threshold value. The threshold is used to group pixels with in the distance. With the proposed technique the features are maintained and the resolution of the image enhanced. The time complexity of the process is reduced. Index Terms:K-means clustering, User Interacted Medical image Segmentation, Threshold based segmentation, Weight Map, histogram, edge detection.

Keywords


K-means Clustering, User Interacted Medical Image Segmentation, Threshold Based Segmentation, Weight Map, Histogram, Edge Detection

References