K.Renuka’s scientific contributions

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


Fig. 2. (a) Original image. (b) Manual segmentation made by an expert radiologist. (c) Liver surface initialization for active contour. (d) Liver surface initialization for graph-cut (in white: liver, in gray: background and in black: undetermined voxels). (e) Graph cut segmentation. (f) Geodesic Graph-cut segmentation (the plain line represents the liver surface, while the dashed line represents the tumor contour).  
Fig. 3. Gray-level histograms related to structural components of: (a) segmented liver, (b) histogram of whole liver, (c) zooming of histogram related to tumors, and (d) zooming of histogram related to vessels.  
Figure l. Flowchart of the automatic liver initialization method.  
Liver And Hepatic Tumors Segmentation in 3-D CT Images
  • Article
  • Full-text available

February 2014

·

303 Reads

·

70 Citations

·

D.L.Roshni Bai

·

K.Renuka

·

[...]

·

C.Savithra

Medical imaging is an important technique for diagnosis and treatment planning today. A new proposed method of fully automatic processing frameworks is given based on graph-cut and Geodesic Graph cut algorithms. This paper addresses the problem of segmenting liver and tumor regions from the abdominal CT images. A predicate is defined for measuring the evidence for a boundary between two regions using Geodesic Graph-based representation of the image. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. Liver and hepatic tumor segmentation can be automatically processed by the Geodesic graph-cut based method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detections rate. Second stage is liver segmentation; the liver region has been segmented using the algorithm of the proposed method. The next stage tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image.

Download

Fig. 2 (a) Input Image. (b) Liver Seed Region. (c) Histogram of the Liver Region (d)Segmented Liver Region. (e)Final Tumor Contour (f) Finally Segmented Liver and Tumor
Interactive Automatic Hepatic Tumour CT Image Segmentation

January 2014

·

131 Reads

·

68 Citations

SSRN Electronic Journal

The problem of interactive foreground/background segmentation in still images is of great practical importance in image editing. They avoid the boundary-length bias of graph-cut methods and results in increased sensitivity to seed placement. A new proposed method of fully automatic processing frameworks is given based on Graph-cut and Geodesic Graph cut algorithms. This paper addresses the problem of segmenting liver and tumor regions from the abdominal CT images. The lack of edge modelling in geodesic or similar approaches limits their ability to precisely localize object boundaries, something at which graph-cut methods generally excel. A predicate is defined for measuring the evidence for a boundary between two regions using Geodesic Graph-based representation of the image. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. Liver and hepatic tumor segmentation can be automatically processed by the Geodesic graph-cut based method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the pre-processing stage, Mean shift filter is applied to CT image process and statistical thresholding method is applied for reducing processing area with improving detections rate. In the Second stage, the liver region has been segmented using the algorithm of the proposed method. Next, the tumor region has been segmented using Geodesic Graph cut method. Results show that the proposed method is less prone to shortcutting than typical graph cut methods while being less sensitive to seed placement and better at edge localization than geodesic methods. This leads to increased segmentation accuracy and reduced effort on the part of the user. Finally Segmented Liver and Tumor Regions were shown from the abdominal Computed Tomographic image.

Citations (2)


... Finally Segmented Liver and Tumor Regions were shown from the abdominal Computed Tomographic image. [45] proposed a system, in which a predicate is defined for measuring the evidence for a boundary between two regions using Geodesic Graph-based representation of the image. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. ...

Reference:

Call for Papers - Journal of Electrical Systems (JES), E-ISSN: 1112-5209, indexed in Scopus & Web of Science
Liver And Hepatic Tumors Segmentation in 3-D CT Images

... The false positive (FP) is reduced and sensitivity and specificity improved by multiple MTANN. [44] proposed a system, this system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the pre-processing stage, Mean shift filter is applied to CT image process and statistical thresholding method is applied for reducing processing area with improving detections rate. ...

Interactive Automatic Hepatic Tumour CT Image Segmentation

SSRN Electronic Journal