INTELLIGENT DETECTION AND CLASSIFICATION OF MICROCALCIFICATION IN COMPRESSED MAMMOGRAM IMAGE
Keywords:classification, compression, mammogram image, micro-calcification, multi-wavelet, region of interest
The main contribution of this article is introducing an intelligent classifier to distinguish between benign and malignant areas of micro-calcification in companded mammogram image which is not proved or addressed elsewhere. This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way. Moreover it gives good classification responses for compressed mammogram image. The goal of the proposed method is twofold: one is to preserve the details in Region of Interest (ROI) at low bit rate without affecting the diagnostic related information and second is to classify and segment the micro-calcification area in reconstructed mammogram image with high accuracy. The prime contribution of this work is that details of ROI and Non-ROI regions extracted using multi-wavelet transform are coded at variable bit rate using proposed Region Based Set Partitioning in Hierarchical Trees (RBSPIHT) before storing or transmitting the image. Image reconstructed during retrieval or at the receiving end is preprocessed to remove the channel noise and to enhance the diagnostic contrast information. Then the preprocessed image is classified as normal or abnormal (benign or malignant) using Probabilistic neural network. Segmentation of cancerous region is done using Fuzzy C-means Clustering (FCC) algorithm and the cancerous area is computed. The experimental result shows that the proposed model performance is good at achieving high sensitivity of 97.27%, specificity of 94.38% at an average compression rate and Peak Signal to Noise Ratio (PSNR) of 0.5bpp and 58dB respectively.
Bao ZL, Chuan XY, Hong SW (2006). New region of interest image coding based on multiple bitplanes up–down shift using improved SPECK algorithm. In: Proc 1st Inter Conf Innovative Computing, Information and Control, 2006 Aug 30-Sep 1; Beijing, 3:629–32.
Bezdek JC (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York Tariq Rashid: 174-92.
Boccignone G, Chianese A, Picariello A (2000). Computer aided detection of microcalcifcations in digital mammograms. Comput Biol Med 30: 267–86.
Chan HP, Doi K, Galhotra, S, Vyborny CJ, MacMahon H, Jokich PM (1987). Image feature analysis and computer-aided diagnosis in digital radiography.I. Automated detection of micro calcifications in mammography. Med Phys 14: 538–48.
Cheng HD, Cai X, Chen X, Hu L, Lou X (2003). Computer-aided detection and classification of micro-calcifications in mammograms: a survey. Pattern Recogn 36 :2967–91.
Cheng HD, Wang J, Shi X, (2004). Micro-calcification detection using fuzzy logic and scale space approaches. Pattern Recogn 37: 363–75.
Christopoulos C, Skodras A, Ebrahimi T (2000). The jpeg2000 still image coding system: an overview. IEEE T Consum Electr 46: 1103–27.
Davies DH, Dance DR, (1992). The automatic computer detection of subtle calcifications in radiographically dense breasts. Phys Med Biol. 37: 1385–90.
Dhawan AP , Royer EL (1988). Mammographic feature enhancement by computerized image processing, Comput Meth Prog Bio 27: 23–35.
Duchowski AT, McCormick BH (1995). Simple Multiresolution approach for representing multiple regions of interest (ROIs).Visual Communications and Image Processing 2501:175–86.
Dunn JC (1973). A fuzzy relative of isodata process and its use in detecting compact well-separated clusters. Cyb 3: 32–57.
Geronimo JS, Hardin DP, Massopust PR (1994). Fractal functions and wavelet expressions based on several scaling functions. J Approx Theory 78: 373–401.
Haralick, RM, Shanmugan K, Dinstein I (1973). Textural features for image classification. IEEE T Syst Man and Cyb. 3: 610-21.
Hsu WY (2012). Improved watershed transform for tumor segmentation. Application to mammogram image compression. Expert Syst Appl 39 : 3950–55.
Jae LS (1990) .Two-dimensional signal and image processing. Englewood Cliffs, NJ, Prentice Hall: 469-76.
Kamangar F, Dores GM, Anderson WF, (2006). Patterns of cancer incidence, mortality, and prevalence across five continents: Defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol 24: 2137–50.
Kestener P, Lina JM, Saint M, Arneodo A (2001). Wavelet based multifractal formalism to assist in diagnosis in digitized mammograms. Image Anal Stereol 20: 169-174
Koning DHJ, Fracheboud J, Boer R, Verbeek ALM, Collette HJ, Hendriks JHCL et al. (1995). Nation-wide breast cancer screening in the Netherlands: support for breast cancer mortality reduction. National evaluation team for breast cancer screening. Int J Cancer 60: 777–80.
Lee SK, Lo CS, Wang CW, Chung PC, Chang CI, Yang CW et al. (2000). A computer-aided design mammography screening system for detection and classification of micro-calcifications. Int J Med Inform 60: 29–57.
Liu Y, Pearlman W (2006). Region of interest access with three-dimensional SBHP algorithm. Vis Commun Image Process 6077: 17– 9.
Mallat SG (1989). A theory for multiresolution signal decomposition:the wavelet representation. IEEE T Pattern Anal 11: 674–93.
Michael H, Torosian MD (2002). Breast Cancer, A guide to detection and multidisciplinary detection. Springer ISBN: 978-1-61737-216-2.
Mousa R, Munib Q, Moussa A (2005). Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Syst Appl 28: 713–23.
Nascimento MZD, Martins AS, Neves LA, Ramos RP, Flores EL, Carrijo GA ( 2013). Classification of masses in mammographic image using wavelet domain features and polynomial classifier. Expert Syst Appl 40: 6213–21.
Nunes FLS, Schiabel H, Benatti RH (1999). Application of image processing techniques for contrast enhancement in dense breasts digital mammograms. In:Medical Imaging. Proc of the SPIE Conference on Image Processing,1999 May 21; San Diego, 3661:1105–16.
Oliver A, Freixenet J, Martí J, Perez E, Pont J, Denton ERE et al. (2010). A review of automatic mass detection and segmentation in mammographic images. Medical Image Anal 14: 87–110.
Olson SL, Fam BW, Winter PF, Scholz FJ, Lee AK, Gordon SE (1988). Breast calcifications: analysis of imaging properties. Radiology 169: 329–32.
Otsu N (1979). A threshold selection method from gray-level histograms. IEEE T Syst Man Cyb 9: 62-66.
Penedo M, Lado MJ, Pablo G, Tahoces (2006). Effects of JPEG2000 data compression on an automated system for detecting clustered micro-calcifications in digital mammograms. IEEE T Inf Technol B 10: 354-61.
Perlmutter SM, Cosmanb PC, Gray RM, Olshend RA, Ikeda D, Adamsf CN et al. (1997). Image quality in lossy compressed digital mammograms. Signal Process 59: 189-210.
Rapesta BJ, Sagrista SJ, Llinas AF (2011). JPEG2000 ROI coding through component priority for digital mammography. Comput Vis Image Und, 115: 59-68.
Said A, Pearlman W (1996) A new, fast and efficient image codec based on set hierarchical trees. IEEE T Circ Syst Vid 6:243–50.
Sarvestan SA, Safavi AA, Parandeh MN, Salehi M (2010). Predicting breast cancer survivability using data mining techniques.In: Proc 2nd Inter conf on software technology and engineering (ICSTE) 2: 227–31.
Shen LX , Tan HH, Tham JY (2000). Symmetric-antisymmetric orthogonal multiwavelets and related scalar wavelets. App and Comput Harmonic Analysis 8:258–79.
Sherrod PH (2012). DTREG predictive modeling software. Http://www.dtreg.com. Accessed on November 2013.
Sickles EA (1997). Breast cancer screening outcomes in women ages 40–49: clinical experience with service screening using modern mammography. Nat Cancer Institute Monographs 22: 99–104.
Somasundaram K, Palaniappan N (2011). Adaptive low bit rate facial feature enhanced residual image coding method using SPIHT for compressing personal ID images. Inter J Electronics Commun 65: 589–94.
Strela V, Heller PN, Strang G, Topiwala P, Heil C (1999). The application of multiwavelet filter banks to image processing. IEEE T Image Process 8: 548–63.
Sucklin J, Parker J, Dance DR, Astley SM, Hutt I, Boggis CRM et al. (1994). The Mammographic Image Analysis Society digital mammogram database. In:Proc International workshop on digital mammography: 211-21.
Tahmasbi A, Saki F, Shokouhi SB (2011). Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 41: 726–35.
Tasdoken S, Cuhadar A (2003). ROI coding with integer wavelet transforms and unbalanced spatial orientation trees. In: Engineering in Medicine and Biology Society. Proc 25th Ann Inter Conf IEEE, 2003 Sep 17-21: 841–44.
Tzikopoulos SD, Mavroforakis ME, Georgioua HV, Dimitropoulos N, Theodoridis S, (2011). A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput Meth Prog bio 102: 47–63.
Verma B (2008). Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms. Artif Intell Med 42: 67–79.
Verma B , Zakos J (2001). A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques IEEE T Inf Technol B 5: 46–54.
Xie G, Shen H (2004). A highly scalable SPECK image coder. In: Proc IEEE Inter Conf Image Processing 2:1297–300.
Yoshida H, Doi K, Nishikawa RM, Giger ML, Schmidt RA (1996). An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered micro-calcifications in digital mammograms. Acad Radiol 3: 621–27.
Yu S, Guan L. (2000). A CAD system for the automatic detection of clustered micro-calcifications in digitized mammogram. IEEE T Med Imaging 19: 115–26.
Yua Q, Cai C , Xiao H, Liu X, Wen Y (2007). Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. Commun Comp Inform Science 2:1250–60.
Yushin C, Pearlman W (2007). Hierarchical dynamic range coding of wavelet subbands for fast and efficient image decompression. IEEE T Image Process 16: 2005–15.
Zadeh HS, Rad FR, Nejad PSD (2004). Comparison of multiwavelet, wavelet, Haralick, and shape features for micro-calcification classification in mammograms. Pattern Recogn 37: 1973–86.