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Kidney Tumor Segmentation and Classification on Abdominal CT Scans
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 164 - Number 9 |
| Year of Publication: 2017 |
| Authors: Bansari Shah, Charmi Sawla, Shraddha Bhanushali, Poonam Bhogale |
10.5120/ijca2017913691
|
Bansari Shah, Charmi Sawla, Shraddha Bhanushali, Poonam Bhogale . Kidney Tumor Segmentation and Classification on Abdominal CT Scans. International Journal of Computer Applications. 164, 9 ( Apr 2017), 1-5. DOI=10.5120/ijca2017913691
Abstract
This paper, deals with systematic study of simple segmentation and classification algorithms for kidney tumor using Computed Tomography images. Tumors are of different types having different characteristics and also have different treatment. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. This CT scans are visually examined by the physician for detection and diagnosis of kidney tumor. However this method lacks accuracy and detection of size of the tumor. So to overcome this, a computer aided segmentation technique has been proposed, which extracts the tumor part from the kidney, further on which feature extraction method is performed for extracting certain features and the type of tumor i.e. malignant or benign is displayed by using simple classifiers .
References
- Anton Bardera, Jaume Rigau, Imma Boada, Miquel Feixas, and Mateu Sbert, “ Image Segmentation Using Information Bottleneck Method ”,Page Number 1601-1612, IEEE Transactions on Image Processing, Vol. 18, No. 7, July 2009.
- Mr.Rohit S. Kabade and Dr. M. S. Gaikwad, “Segmentation of Brain Tumour and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy CMean Algorithm ” IJCSET Vol. 4 No. 05 May 2013
- J.selvakumar ,A.Lakshmi and T.Arivoli.”Brain Tumor Segmentation and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm"IEEE- ICAESM -2012 March 30, 31, 2012
- Robert.L.Cannon;Jitendra.V.Dave;James.C.Bezdek”Efficient Implementation of the Fuzzy c-Means Clustering Algorithms”IEEE TRANSACTFIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. VOL. PAMI-8, NO. 2, MARCH 1986
- Kuldeep Pawar , Jayamala.K.Patil “A Systematic Study of Segmentation Methods For Detection of Kidney Tumor Using Computed Tomography Images.”International Journal of Engineering Sciences & Research Technology Nov., 2012
- Mredhula.L and Dr.M.A.Dorairangaswamy “Detection And Classification of Tumors in CT Images” IJCSE Vol. 6 No.2 Apr-May 2015
- U Akilandeswari, Nithya Rajendran and B Santhi, “Review on Feature Extraction Methods in Pattern Classification”, European Journal of Scientific Research, Vol.71 No.2 (2012).
- Kittipong Chomboon* , Pasapitch Chujai, Pongsakorn Teerarassamee, Kittisak Kerdprasop, Nittaya Kerdprasop, “An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm “, International Conference on Industrial Application Engineering 2015
- Jian Wu, Feng Ye, Jian-Lin Ma, Xiao-Ping Sun, Jing Xu, Zhi-Ming, “ The Segmentation and Visualization of Human Organs Based on Adaptive Region Growing Method” ,Page Number-439-443, IEEE 8th International Conference on Computer and Information Technology Work shops978-0-7695-3242-4/08,IEEE,2008.
- Rafael C. Gonzalez, Richard E.Woods, “ Digital Image processing”, published by Pearson Education, Inc. 2002.
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