Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation

The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists.

[1]  L. Riggs,et al.  Curvature detectors in human vision? , 1974, Science.

[2]  Dale Schuurmans,et al.  Fast normalized cut with linear constraints , 2009, CVPR.

[3]  A. Bleasel,et al.  Cerebral and Cerebellar Volume Reduction in Children with Intractable Epilepsy , 2000, Epilepsia.

[4]  H L Kundel,et al.  A visual concept shapes image perception. , 1983, Radiology.

[5]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[6]  Walter H. Ehrenstein,et al.  Basics of seeing motion , 2003 .

[7]  Francesc Massanes,et al.  Motion perception in medical imaging , 2011, Medical Imaging.

[8]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[9]  J. Mixter.  Fast , 2012 .

[10]  Stephen M. Pizer,et al.  Fundamental properties of medical image perception , 1991, Journal of Digital Imaging.

[11]  Lihi Zelnik-Manor,et al.  Saliency for image manipulation , 2013, The Visual Computer.

[12]  E A Krupinski,et al.  Perception research in medical imaging. , 2005, The British journal of radiology.

[13]  G. Hagemann,et al.  Fast, accurate, and reproducible automatic segmentation of the brain in T1‐weighted volume MRI data , 1999, Magnetic resonance in medicine.

[14]  Sung Wook Baik,et al.  MRT letter: Visual attention driven framework for hysteroscopy video abstraction , 2013, Microscopy research and technique.

[15]  Dale Schuurmans,et al.  Fast normalized cut with linear constraints , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Tara Estlin,et al.  Rover traverse science for increased mission science return , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[17]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[18]  Guang H. Yue,et al.  Automated Histogram-Based Brain Segmentation in T1-Weighted Three-Dimensional Magnetic Resonance Head Images , 2002, NeuroImage.

[19]  E. Krupinski,et al.  The importance of perception research in medical imaging. , 2000, Radiation medicine.

[20]  S Coren,et al.  The effects of perceptual set on the shape and apparent depth of subjective contours , 1986, Perception & psychophysics.

[21]  Yacine Rezgui,et al.  Categorization of malicious behaviors using ontology-based cognitive agents , 2013, Data Knowl. Eng..

[22]  S. Zucker,et al.  Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.

[23]  David A. Rottenberg,et al.  Quantitative comparison of four brain extraction algorithms , 2004, NeuroImage.

[24]  H. Wilson,et al.  Concentric orientation summation in human form vision , 1997, Vision Research.

[25]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[26]  L. Heier,et al.  Detection of brain metastases: comparison of contrast-enhanced MR with unenhanced MR and enhanced CT. , 1990, AJNR. American journal of neuroradiology.

[27]  E. Krupinski,et al.  Eye-movement study and human performance using telepathology virtual slides: implications for medical education and differences with experience. , 2006, Human pathology.

[28]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[29]  D. Deng,et al.  How interesting is this? Finding interest hotspots and ranking images using an MPEG-7 visual attention model , 2005 .

[30]  Peter J. Bickel,et al.  The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[31]  Sally Freels,et al.  Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. , 2010, Neuro-oncology.

[32]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[33]  R. von der Heydt,et al.  Illusory contours and cortical neuron responses. , 1984, Science.

[34]  Frédo Durand,et al.  A Benchmark of Computational Models of Saliency to Predict Human Fixations , 2012 .

[35]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[36]  J M Wolfe,et al.  Curvature is a Basic Feature for Visual Search Tasks , 1992, Perception.

[37]  R. Casta,et al.  Validating Rover Image Prioritizations , 2005 .

[38]  In-Young Kim,et al.  Adaptable fuzzy C-Means for improved classification as a preprocessing procedure of brain parcellation , 2010, Journal of Digital Imaging.

[39]  Samia Nefti-Meziani,et al.  iDetect: Content Based Monitoring of Complex Networks using Mobile Agents , 2012, Appl. Soft Comput..

[40]  Sung Wook Baik,et al.  Efficient visual attention based framework for extracting key frames from videos , 2013, Signal Process. Image Commun..

[41]  Anders P. Eriksson,et al.  Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[42]  J. Loh,et al.  Tumor volume and uterine body invasion assessed by MRI for prediction of outcome in cervical carcinoma treated with concurrent chemotherapy and radiotherapy. , 2007, Japanese journal of clinical oncology.

[43]  Jianbo Shi,et al.  Segmentation given partial grouping constraints , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Sung Wook Baik,et al.  Automatic Segmentation of Region of Interests in MR Images Using Saliency Information and Active Contours , 2012, ICITCS.

[45]  Harold Pashler,et al.  A Boolean map theory of visual attention. , 2007, Psychological review.