1. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Old Dominion University (ODU) Norfolk, Virginia Presenter: Dr. Abu Asaduzzaman Assistant Professor of Computer Architecture and Director of CAPPLab Department of Electrical Engineering and Computer Science (EECS) Wichita State University (WSU), Wichita, Kansas April 1, 2016

2. Dr. Zaman2 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Outline► ■Introduction  Breast cancer and some related information (Q/A) ■‘Digital’ Mammogram Image  Mammogram Image to Digital Matrices ■Fast Effective Analysis  Existing Methods  Problem Description, Proposed Method, Key Contributions  Preliminary Results  What is next? ■Computer Arch and Parallel Prog Lab (CAPPLab)  Laboratory, Research ■Discussion QUESTIONS? Any time, please!

3. Dr. Zaman3 Thank you, all! ■ECE Department, ODU Dr. Dimitrie C. Popescu Khan M. Iftekharuddin

4. Dr. Zaman4 Introduction ■Dr. (Ph.D.) vs Doctor (M.D.) ■Doctor of Philosophy Vs Doctor of Medicine ■Computer Engineers/Scientists Vs Cancer Physicians Vs ■Dr. David Patterson, University of California at Berkeley (UCB) [1]  Algorithms, Machines, and People Laboratory (AMPLab)  3 D., 10 F., 2 V.R., 7 P.D., 44 GR, and 2 UG ■Brown University and Washington University in St. Louis [2, 3] ■Vanderbilt-Ingram Cancer Center at Vanderbilt University, Nashville [4] “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

5. Dr. Zaman5 Introduction (Q/A) Cancer – Types – Rank ■Cancer is the name given to a collection of related diseases. In all types of cancer, some of the body’s cells begin to divide without stopping and spread into surrounding tissues [1]. It’s all bad! ■This uncontrollable cell division turns into tumors or lump. Two types tumors are common: Benign tumor and Malignant tumor [2]. ■Cancer is suspected to become the leading cause of death (2.3 million new cancer cases) in the United States by 2030 [4]. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

6. Dr. Zaman6 Introduction (Q/A) Benign Vs Malignant “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

7. Dr. Zaman7 Introduction (Q/A) Breast Cancer – Mobility ■Breast cancer is a malignant tumor that starts in the cells of the breast, commonly in women. ■According to mobility, breast cancers are two types: Non-Invasive and Invasive [3]. Normal milk-duct Non-Invasive Invasive “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

8. Dr. Zaman8 Introduction (Q/A) Breast Cancer – Severity ■Breast cancer is most common in American women. ■According to recent reports, about 231,840 new cases of invasive breast cancer are diagnosed in women and about 40,290 women died from breast cancer in 2015. ■About 12% (one out of eight) U.S. women are suspected to grow breast cancer in their life-times. ■Many of them are suspected to die due to the breast cancer. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

9. Dr. Zaman9 Introduction (Q/A) Breast Cancer – Common Practice ■The white dots present in the image are tumors. These tumors contain Calcium which makes the tumor brighter than the surrounding. ■Commonly used image types include magnetic resonance imaging (MRI) and mammogram (soft x-ray) [3]. MRI Image “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

10. Dr. Zaman10 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Outline► ■Introduction  Breast cancer and some related information (Q/A) ■‘Digital’ Mammogram Image  Mammogram Image to Digital Matrices ■Fast Effective Analysis  Existing Methods  Problem Description, Proposed Method, Key Contributions  Preliminary Results  What is next? ■Computer Arch and Parallel Prog Lab (CAPPLab)  Laboratory, Research ■Discussion QUESTIONS?Any time, please!

11. Dr. Zaman11 Mammogram Images ■MRI (a medical imaging technique for radiology) uses strong magnetic fields, radio waves, and field gradients to form images of the body. ■Mammography (aka, mastography) is the process of using low-energy X-rays (around 30 Peak kilovoltage (kVp)) ■Digital mammography is a specialized form of mammography that uses digital receptors and computers instead of x-ray film to help examine breast tissue for breast cancer. ■‘Digital’ mammogram image is a digital matrix generated for each region of interest (ROI) using the equivalent pixel values. MRI Image “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

12. Dr. Zaman12 Digital Mammogram Image ■Mammogram imaging technique uses low-power x-ray (~ 30 kVp). ■A mammogram image can be 8-bit, 12-bit, or 16-bit depth. ■A smallest addressable point of mammogram is called pixel. Each pixel can have a value depends upon bit depth. ■For example, an 8-bit pixel value ranges between 0 and 255; where 0 represents pure black and 255 pure white. Similarly, an 16-bit pixel value ranges between 0 and 65535. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Digital Mammogram Image

13. Dr. Zaman13 ‘Digital’ Mammogram Image MRI Image “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Digital Mammogram Image ‘Digital’ Mammogram Image Digital Matrix of Pixel Values

14. Dr. Zaman14 Existing Methods Computer Aided Diagnosis (CAD) ■Current Images: Four pictures taken from different angle. ■Previous Images: Patients old pictures. ■Anamnesis Data: Includes patients family records and personal health parameters ■Preprocessing : Image enhancement and basic parenchymal pattern determined. CAD System “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

15. Dr. Zaman15 Existing Methods Computer Aided Diagnosis (CAD) ■Detection: Several algorithm applied here for including neural network,texture analysis, heuristic methods, wavelet transform based method etc. to identify the tumors. ■Validation: If suspicious location detected then again an algorithm is used to justify or reject the detection. ■Classification: This consists of findings and probability like measure reliability of the diagnosis [5]. CAD System “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

16. Dr. Zaman16 Existing Methods Problems with CAD ■Computer Aided Diagnosis system has 72.55% accuracy. Due to this reason, radiologists need to review after each screening process. ■Breast cells are consists of fatty tissue, CAD system is unable to separate fatty tissue and microcalcifications. ■Mammogram or breast tomosynthesis has high rate of false positive and false negative. As a result many people undergoing delayed treatment. CAD System “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

17. Dr. Zaman17 Existing Methods Fourier transform and log transform [8] ■Pros: This method is very easy to program. Digital image can be used in this technique. It takes very less time to process the image. ■Cons: The output image is very unclear, this method o detects the tumors or the mass regardless it is malignant or benign. Size of the tumor is unknown. Algorithm “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

18. Dr. Zaman18 Existing Methods Top hat Image processing ■Pros: Malignant and benign differentiation is considered. ■Cons: Step 3 fill the gaps between suspected regions may give a wrong result. Sometimes it may change the actual tumor size. The output they have produced has major drawbacks. Algorithm “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

19. Dr. Zaman19 Existing Methods Matlab & LabVIEW ■In this paper, authors try to highlight the suspicious region and extracted from the main image. Then they have done a few feature extraction and record the malignant and benign tumors properties or features [9]. ■Drawbacks: Breast cancer is not easily predictable. They have tested their algorithm on 20 patients which is very less to say something strongly about any feature. They have not validate their algorithm that is sensitivity to tumor or false positive calculation. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

20. Dr. Zaman20 Problem Description ■Existing methods to analyze breast cancer are not 100% effective! Three points: accuracy, faster results, and cost Major Contributions ■By processing mammogram images, classify fatty tissues of (benign and malignant) tumors. ■By processing mammogram images, select region of interest (ROI) and generate matrix of pixel values for each ROI. ■Using ROIs and matrices, extract geometrical (example: area of tumor) and textural (example: mean pixel value) features. ■For faster processing of mammogram images, discover/apply multithreaded GPU-assisted parallel programming techniques. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

21. Dr. Zaman21 Proposed Method ■Work-flow  Breast mammography images are taken.  Each image is ‘pre-processed’ to make it ready ROI selection.  ROIs are selected.  Matrix for each ROI is generated using the pixel values.  Feature values (tumor area, mean pixel value, etc.) are extracted.  Feature values are analyzed for accurate assessment.  GPU-assisted high performance computing are applied for faster and cheaper analysis/solution. ■Software/Tools  Matlab  CUDA/GPU Computing “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

22. Dr. Zaman22 Proposed Method Pre-processing Image “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” 3 Images in each processing stage 4 12 1 2 3 4

23. Dr. Zaman23 Proposed Method Selecting ROI “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Images in each processing stage 4 65 4 5 6 1

24. Dr. Zaman24 Proposed Method Feature Extraction ■Geometrical Features  Area, perimeter, radius, and shape of the tumor ■Textural Features  Statistical functions: mean, global mean, standard devaition, entropy, and skewness of pixel values “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” 6 1

25. Dr. Zaman25 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Outline► ■Introduction  Breast cancer and some related information (Q/A) ■‘Digital’ Mammogram Image  Mammogram Image to Digital Matrices ■Fast Effective Analysis  Existing Methods  Problem Description, Proposed Method, Key Contributions  Preliminary Results  What is next? ■Computer Arch and Parallel Prog Lab (CAPPLab)  Laboratory, Research ■Discussion QUESTIONS?Any time, please!

26. Dr. Zaman26 Preliminary Results Area of tumor (a geometrical feature) The area value due to a benign tumor is between 0 and 12191. The area value due to a malignant tumor is between 907 and 95114. Therefore, we consider area value as a lower decision making factor. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” A benign tumor shows area value in the malignant tumor region. (Not correct!) A malignant tumor shows area value in the benign tumor region. (Not correct!)

27. Dr. Zaman27 Preliminary Results Mean of pixel values (a textural feature) The mean pixel value due to a benign tumor is between 95 and 155. The mean pixel value due to a malignant tumor is between 175 and 195. Therefore, we consider mean pixel value as a higher decision making factor. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

28. Dr. Zaman28 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Preliminary Results Radius of tumor (a geometrical feature) The radius value due to a benign tumor is between 0 and 69. The radius value due to a malignant tumor is between 17 and 174. Therefore, we consider radius value as a lower decision making factor.

29. Dr. Zaman29 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Preliminary Results Perimeter of tumor (a geometrical feature) The perimeter value due to a benign tumor is between 0 and 1237. The perimeter value due to a malignant tumor is between 106 and 1093. Therefore, we consider perimeter value as a lower decision making factor.

30. Dr. Zaman30 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Preliminary Results Global mean of pixel values (a textural feature) The global mean value due to a benign tumor is between 1.0 and 1.1856. The global mean value due to a malignant tumor is between 2.3 and 4.2. Therefore, we consider global mean value as a higher decision making factor.

31. Dr. Zaman31 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Preliminary Results Standard Deviation (S.D.) of pixel values (a textural feature) The standard deviation value due to a benign tumor is less than 10. The standard deviation value due to a malignant tumor is between 20 and 32. Therefore, we consider standard deviation value as a higher decision making factor.

32. Dr. Zaman32 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” What do we have? What do we do with it?

33. Dr. Zaman33 Problem Description ■Existing methods to analyze breast cancer are not 100% effective! Three points: accuracy, faster results, and cost Major Contributions ■By processing mammogram images, classify fatty tissues of (benign and malignant) tumors. ■By processing mammogram images, select region of interest (ROI) and generate matrix of pixel values for each ROI. ■Using ROIs and matrices, extract geometrical (example: area of tumor) and textural (example: mean pixel value) features. ■For faster processing of mammogram images, discover/apply multithreaded GPU-assisted parallel programming techniques. “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

34. Dr. Zaman34 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” What do we do with it? ■By processing many mammogram images, we plan to classify fatty tissues of (benign and malignant) tumors.

35. Dr. Zaman35 What is next? ■High Performance Computing in Healthcare Technology Customize image processing / pattern recognition for cancer research Discover / apply GPU-assisted multithreaded parallel programming Apply data regrouping and task/thread regrouping based optimization “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment”

36. Dr. Zaman36 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Outline► ■Introduction  Breast cancer and some related information (Q/A) ■‘Digital’ Mammogram Image  Mammogram Image to Digital Matrices ■Fast Effective Analysis  Existing Methods  Problem Description, Proposed Method, Key Contributions  Preliminary Results  What is next? ■Computer Arch and Parallel Prog Lab (CAPPLab)  Laboratory, Research ■Discussion QUESTIONS?Any time, please!

37. Dr. Zaman37 CAPPLab ■Computer Architecture & Parallel Programming Laboratory (CAPPLab)  Physical location: 245 Jabara Hall, Wichita State University  URL: http://www.cs.wichita.edu/~capplab/http://www.cs.wichita.edu/~capplab/  E-mail: capplab@cs.wichita.edu; Abu.Asaduzzaman@wichita.educapplab@cs.wichita.eduAbu.Asaduzzaman@wichita.edu  Tel: +1-316-WSU-3927 ■Key Objectives  Lead research in advanced-level computer architecture, high- performance computing, embedded systems, and related fields.  Teach advanced-level computer systems & architecture, parallel programming, and related courses. Computer Architecture and Parallel Programming Laboratory (CAPPLab)

38. Dr. Zaman38 “People First” ■Students  PhD Students: Kishore K. Chidella, Ahmed E. Aziz  MS Theses: Parthib Mitra, Shanta Mazumder, Jainish R. Jain  MS Projects: Venkatesh Mabbu, Avinash Chintam  UG Students: Suveen R. Emmanuel ■Collaborators  Dr. H. Neeman, Director of OSCER, University of Oklahoma, OK  Dr. M. Islam, Hematology/Oncology Specialist, UPMC, PA  Dr. Larry Bergman, NASA Jet Propulsion Laboratory (JPL), CA  Mr. J. Metrow, Director of HiPeCC, Wichita State Univ. (WSU), KS  Dr. K. Cluff, Ast. Prof. of Biomedical Engineering, WSU, KS Computer Architecture and Parallel Programming Laboratory (CAPPLab)

39. Dr. Zaman39 Resources ■Hardware  3 CUDA Servers – CPU: Xeon E5506, 2x 4-core, 2.13 GHz, 8GB DDR3; GPU: GTX Titan X (24x 128 cores, 12GB GDDR5), Telsa K40 (15x 192 cores, 12GB GDDR5) and C2075 (14x 32 cores, 6GB GDDR5)  Supercomputer (Opteron 6134, 32 cores per node, 2.3 GHz, 64 GB DDR3, Kepler card) via remote access to WSU (HiPeCC)  2 CUDA enabled Laptops  More … ■Software  CUDA, OpenMP, and Open MPI (C/C++ support)  MATLAB, VisualSim, CodeWarrior, more (as may needed) Computer Architecture and Parallel Programming Laboratory (CAPPLab)

40. Dr. Zaman40 Scholarly Activities ■NVIDIA “GPU Research Center” for 2015-2017  Grants from NSF, NetApp, CybertronPC, Wiktronics, M2SYS  Research in Computer Architecture and Parallel Programming ■Publications  Journal: 21 published; 1 under review, 3 under preparation  Conference: 57 published, 4 accepted, 2 under review, 6 under pre  Book Chapter: 2 published; 1 under preparation ■Outreach  USD 259 Wichita Public Schools  Wichita Area Technical and Community Colleges  Open to collaborate Computer Architecture and Parallel Programming Laboratory (CAPPLab)

41. Dr. Zaman41 Research Grants/Proposals ■Grants/Awards  WSU: URCA, Flossie West  NSF: KS NSF EPSCoR First Award  NetApp: NFS Connector for Spark Systems  NVIDIA GPU Research Center at Wichita State Award  Xilinx University/Teaching (Hardware/Financial) Award ■Proposals  WSU: URCA, Flossie West  NSF: XPS, IUSE  U.S. Govt.: DoD Breast Cancer Research Program  Industry: CybertronPC, American Association for Cancer Research Computer Architecture and Parallel Programming Laboratory (CAPPLab)

42. Old Dominion University (ODU), Norfolk, Virginia, 2016 “Fast Effective Analysis of ‘Digital’ Mammogram Images for Breast Cancer Treatment” Thank You! QUESTIONS? Feedback? Contact: Abu Asaduzzaman E-mail: abuasaduzzaman@ieee.org Phone: +1-316-978-5261 http://www.cs.wichita.edu/~capplab/