Thresholding and morphological based segmentation techniques for medical images

The main objective of this work is to segment the medical image under various conditions and different backgrounds. Image segmentation is very useful and it improves the results of image analysis. Segmentation done manually is not an easy task also it consume a lots of time. Its accuracy percentage is also very less. So, there is a necessity of developing accurate and efficient algorithms for medical image segmentation. A new algorithm for segmentation of MRI and CT images has been proposed in this work, based on thresholding and morphological techniques. The proposed algorithm has been verified on Brain MRI and CT Angiography. It has been proved that the proposed method is better than the existing techniques. This verification is done on the basis of the performance parameters such as completeness and correctness.

[1]  Mandava Rajeswari,et al.  Wavelet Energy Guided Level Set for Segmenting Highly Similar Regions in Medical Images , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[2]  G. Thirugnanam,et al.  A dualistic sub-image histogram equalization based enhancement and segmentation techniques for medical images , 2013, 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013).

[3]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[5]  Hairong Zheng,et al.  A Novel Interactive Image Processing Approach for DICOM Medical Image Data , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[6]  DeLiang Wang,et al.  Segmentation of medical images using LEGION , 1999, IEEE Transactions on Medical Imaging.

[7]  P. Schmid.  Segmentation of digitized dermatoscopic images by two-dimensional color clustering , 1999, IEEE Transactions on Medical Imaging.

[8]  Ashwani Kumar Yadav,et al.  Segmentation on moving shadow detection and removal by symlet transform for vehicle detection , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[9]  Ashok Samal,et al.  Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Serge J. Belongie,et al.  Normalized cuts in 3-D for spinal MRI segmentation , 2004, IEEE Transactions on Medical Imaging.

[11]  Milan Sonka,et al.  Segmentation and interpretation of MR brain images. An improved active shape model , 1998, IEEE Transactions on Medical Imaging.

[12]  U Tiede,et al.  3-D segmentation of MR images of the head for 3-D display. , 1990, IEEE transactions on medical imaging.

[13]  LA Vrence.  Segmentation Based on Intensity Extrema , 2004 .

[14]  Ashwani Kumar Yadav,et al.  De-noising of ultrasound image using discrete wavelet transform by symlet wavelet and filters , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[15]  Archek Praveen Kumar,et al.  Survey on Content-based Image Retrieval and Texture Analysis with Applications , 2014 .

[16]  Wanhyun Cho,et al.  Segmentation of medical images using a geometric deformable model and its visualization , 2008, Canadian Journal of Electrical and Computer Engineering.

[17]  Chuan-Yu Chang,et al.  Thyroid segmentation and volume estimation in ultrasound images , 2010, 2008 IEEE International Conference on Systems, Man and Cybernetics.