Lung Nodule Segmentation through Unsupervised Clustering Models

Abstract Image processing is an essential technique for analyzing images. The important part of image processing is image segmentation. Segmentation is a task of grouping pixels based on similarity. In medical image analysis, segmentation is very important phase. In this paper Possibilistic Clustering models, Fuzzy Clustering models and a new approach called Possibilistic-Fuzzy based clustering model are discussed. Experiments are carried out on bench mark medical images to examine the performance of the above techniques. The results are compared with various validation measures to explore the accuracy of our proposed approach.

[1]  Thomas A. Runkler,et al.  Alternating cluster estimation: a new tool for clustering and function approximation , 1999, IEEE Trans. Fuzzy Syst..

[2]  Michael G. Strintzis,et al.  Still Image Segmentation Tools For Object-Based Multimedia Applications , 2004, Int. J. Pattern Recognit. Artif. Intell..

[3]  Hichem Frigui,et al.  Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II , 1995, IEEE Trans. Fuzzy Syst..

[4]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[5]  K. Doi,et al.  Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs. , 1996, Radiology.

[6]  J. Austin,et al.  Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society. , 1996, Radiology.

[7]  H Nishitani,et al.  Screening for lung cancer in a fixed population by biennial chest radiography. , 1983, Radiology.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[10]  J. Woodring,et al.  Pitfalls in the radiologic diagnosis of lung cancer. , 1990, AJR. American journal of roentgenology.

[11]  Miin-Shen Yang A survey of fuzzy clustering , 1993 .

[12]  Thomas A. Runkler,et al.  Function approximation with polynomial membership functions and alternating cluster estimation , 1999, Fuzzy Sets Syst..

[13]  W. E. Miller,et al.  Lung cancer detected during a screening program using four-month chest radiographs. , 1983, Radiology.

[14]  James C. Bezdek,et al.  A mixed c-means clustering model , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[15]  Max A. Viergever,et al.  Computer-aided diagnosis in chest radiography: a survey , 2001, IEEE Transactions on Medical Imaging.

[16]  B. Ginneken Computer-aided diagnosis in chest radiography , 2001 .

[17]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[18]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[19]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[20]  Chronic Disease Division Cancer facts and figures , 2010 .

[21]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..