Tumor Detection in Brain Magnetic Resonance Images Using Modified Thresholding Techniques

Automated computerized image segmentation is very important for clinical research and diagnosis. The paper deals with two segmentation schemes namely Modified Fuzzy thresholding and Modified minimum error thresholding. The method includes the extraction of tumor along with suspected tumorized region which is followed by the morphological operation to remove the unwanted tissues. The performance measure of various segmentation schemes are comparatively analyzed based on segmentation efficiency and correspondence ratio. The automated method for segmentation of brain tumor tissue provides comparable accuracy to those of manual segmentation.

[1]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[2]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[3]  Heng-Da Cheng,et al.  A novel approach to microcalcification detection using fuzzy logic technique , 1998, IEEE Transactions on Medical Imaging.

[4]  Ian Middleton,et al.  Segmentation of magnetic resonance images using a combination of neural networks and active contour models. , 2004, Medical engineering & physics.

[5]  Qin Zhong,et al.  On minimum error thresholding and its implementations , 1988 .

[6]  M L Mendelsohn,et al.  THE ANALYSIS OF CELL IMAGES * , 1966, Annals of the New York Academy of Sciences.

[7]  Josef Kittler,et al.  Threshold selection based on a simple image statistic , 1985, Comput. Vis. Graph. Image Process..

[8]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[9]  Kenong Wu,et al.  Live cell image segmentation , 1995, IEEE Transactions on Biomedical Engineering.

[10]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[11]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[12]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[13]  Samir I. Shaheen,et al.  A Modular Computer Vision System for Picture Segmentation and Interpretation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  C. V. Jawahar,et al.  Investigations on fuzzy thresholding based on fuzzy clustering , 1997, Pattern Recognit..

[15]  Yan Zhu,et al.  Computerized tumor boundary detection using a Hopfield neural network , 1997, IEEE Transactions on Medical Imaging.

[16]  J. Pauly,et al.  A homogeneity correction method for magnetic resonance imaging with time-varying gradients. , 1991, IEEE transactions on medical imaging.

[17]  Lawrence O. Hall,et al.  Automatic segmentation of non-enhancing brain tumors in magnetic resonance images , 2001, Artif. Intell. Medicine.

[18]  Anthony J. Yezzi,et al.  A geometric snake model for segmentation of medical imagery , 1997, IEEE Transactions on Medical Imaging.

[19]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..