Comparison of Segmentation Tools for Multiple Modalities in Medical Imaging

Image segmentation plays a crucial role in many medical imaging applications by extracting the  regions of interest. Accurate segmentation of medical images is a key step in the use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection. In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic tools as well as with some specific algorithms. In this paper various automatic tools as well as manual segmentation algorithms have been used and compared. Both the outcomes either from automatic tool as well as using an algorithm provide the required ROI (region of interest) but automatic tool’s output is more efficient and perfect. 3D visualization as well as volumetric segmentation is done accurately with the help of these tools which help in segmenting CT (3D) images especially.

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

[2]  Donald D Duncan,et al.  Computer-aided assessment of diagnostic images for epidemiological research , 2009, BMC medical research methodology.

[3]  Neeraj Sharma,et al.  Automated medical image segmentation techniques , 2010, Journal of medical physics.

[4]  C E Engeler,et al.  Ground-glass opacity of the lung parenchyma: a guide to analysis with high-resolution CT. , 1993, AJR. American journal of roentgenology.

[5]  R. Vig,et al.  AN EFFICIENT VISUALIZATION AND SEGMENTATION OF LUNG CT SCAN IMAGES FOR EARLY DIAGNOSIS OF CANCER , 2010 .

[6]  G. Ferretti.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists detection performance, K. Awai, K. Murao, A. Ozawa, M. Komi, H. Hayakawa, S. Hori, Y. Nishimura, in: Radiology, 230. (2004), 347 , 2005 .

[7]  K. Awai,et al.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. , 2004, Radiology.

[8]  C Flores-Mir,et al.  Role of different imaging modalities in assessment of temporomandibular joint erosions and osteophytes: a systematic review. , 2008, Dento maxillo facial radiology.

[9]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[10]  Nikos Dimitropoulos,et al.  A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images , 2006, Comput. Methods Programs Biomed..

[11]  Juha Öhman,et al.  Texture analysis of MR images of patients with Mild Traumatic Brain Injury , 2010, BMC Medical Imaging.

[12]  Gurjinder Kaur,et al.  A Study of Gaps in CBMIR Using Different Methods and Prospective , 2008 .

[13]  J. Lew,et al.  Developments in the use of ultrasound for thyroid cancer , 2010, Current opinion in oncology.

[14]  W J Kostis,et al.  Computer-aided diagnosis for lung cancer. , 2000, Radiologic clinics of North America.

[15]  Ziv Yaniv,et al.  Quantitative CT for volumetric analysis of medical images: initial results for liver tumors , 2010, Medical Imaging.

[16]  Jin Mo Goo,et al.  Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. , 2005, Radiology.

[17]  Ghassan Hamarneh,et al.  MATLAB-ITK interface for medical image filtering, segmentation, and registration , 2006, SPIE Medical Imaging.

[18]  Nisar Ahmed Memon,et al.  Segmentation of Lungs from CT Scan Images for Early Diagnosis of Lung Cancer , 2008 .

[19]  S. S. Kumar,et al.  Automatic Segmentation of Liver and Tumor for CAD of Liver , 2011 .

[20]  João Manuel R. S. Tavares,et al.  A Review on the Current Segmentation Algorithms for Medical Images , 2009, IMAGAPP.

[21]  René Tschirley,et al.  Patient-oriented segmentation and visualization of medical data , 2002 .