Tumor detection in medical imaging: a survey

A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases.

[1]  Ronald M. Summers,et al.  Automatic detection of endobronchial lesions using virtual bronchoscopy: comparison of two methods , 1998, Medical Imaging.

[2]  Robin N. Strickland Image-Processing Techniques for Tumor Detection , 2007 .

[3]  Noboru Niki,et al.  Computer aided diagnosis system for lung cancer based on helical CT images , 1997, Medical Imaging.

[4]  Jin Akiyama,et al.  Computational Geometry, Graphs and Applications , 2011, Lecture Notes in Computer Science.

[5]  Zaw Zaw Htike,et al.  Multi-horizon ternary time series forecasting , 2013, 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[6]  Zaw Zaw Htike,et al.  A Monocular View-Invariant Fall Detection System for the Elderly in Assisted Home Environments , 2011, 2011 Seventh International Conference on Intelligent Environments.

[7]  Shoon Lei Win,et al.  Recognition of Promoters in DNA Sequences Using Weightily Averaged One-dependence Estimators , 2013 .

[8]  Zaw Zaw Htike,et al.  Can the future really be predicted? , 2013, 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[9]  Noboru Niki,et al.  Three-dimensional analysis of lung areas using thin slice CT images , 1996, Medical Imaging.

[10]  M. Giger,et al.  Computerized Detection of Pulmonary Nodules in Computed Tomography Images , 1994, Investigative radiology.

[11]  Noboru Niki,et al.  Classification of pulmonary nodules in thin-section CT images based on shape characterization , 1997, Proceedings of International Conference on Image Processing.

[12]  Zaw Zaw Htike,et al.  Vision based entomology – how to effectively exploit color and shape features , 2014 .

[13]  D. Cavouras,et al.  Image analysis methods for solitary pulmonary nodule characterization by computed tomography. , 1992, European journal of radiology.

[14]  Anam Mustaqeem,et al.  An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation , 2012 .

[15]  Tinku Acharya,et al.  Image Processing: Principles and Applications , 2005, J. Electronic Imaging.

[16]  Zaw Zaw Htike,et al.  Classification of Eukaryotic Splice-junction Genetic Sequences Using Averaged One-dependence Estimators with Subsumption Resolution , 2013 .

[17]  Zaw Zaw Htike,et al.  Bacteria identification from microscopic morphology: a survey , 2014, SOCO 2014.

[18]  Zaw Zaw Htike,et al.  Brain tumor detection and localization in magnetic resonance imaging , 2014 .

[19]  Shoon Lei Win,et al.  Cancer recurrence prediction using machine learning , 2014 .

[20]  Zaw Zaw Htike,et al.  Bacteria identification from microscopic morphology using naïve bayes , 2014 .

[21]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[22]  Zaw Zaw Htike,et al.  VISION BASED ENTOMOLOGY : A SURVEY , 2014 .

[23]  Michael F. McNitt-Gray,et al.  Application of image analysis techniques to distinguish benign from malignant solitary pulmonary nodules imaged on CT , 1998, Medical Imaging.

[24]  T. Logeswari,et al.  An improved implementation of brain tumor detection using segmentation based on soft computing , 2010 .

[25]  P Croisille,et al.  Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT. Work in progress. , 1995, Radiology.