A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework

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1 I.J.Modern Educaton and Computer Scence, 2011, 5, Publshed Onlne August 2011 n MECS ( A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on Multple Arbtrary Shape s Codng Framework Png Xu 1, Yan Zuo 2, We-Dong Xu 1, and Hua-Je Chen 2 1: College of Lfe Informaton Scence & Instrument Engneerng, Hangzhou Danz Unversty Hangzhou, Zhejang, Chna xupng@hdu.edu.cn, temco@hdu.edu.cn 2: College of Automaton, Hangzhou Danz Unversty, Hangzhou, Zhejang, Chna yzuo@hdu.edu.cn, chj247@hdu.edu.cn Abstract Wth the rapdly growng use of dgtal mages n medcal archval and communcaton, mage compresson technology, especally dagnoss lossless compresson technology, plays a more and more mportant role for medcal applcatons. In ths thess, a novel dagnoss loseless compresson algorthm s presented for dgtal mammography. The mammogram s dvded nto breast regon, pectoral muscle and background usng the CAD technology. Then mutple arbtrary shape s codng framework s used to compress the mammogram n whch the breast regon and pectoral muscle are compressed losslessly and lossly respectvely, and the background can be dscarded or compressed lossly as user s wll. Expermental results show that the proposed method offer potental advantage n medcal applcatons of dgtal mammography compresson. Index Terms dgtal mammography; dagnoss lossless compresson; CAD; I. INTRODUCTION In the latest twenty years, the mammogram cancer has become one of the most dangerous malgnant tumors of women whose death rato has been more than forty percent. Wth the rapd development of lvng standard and requrements of health care, doctors advse women to take mamograms twce every year. It s estmated that approxmately 10%-30% of breast cancer cases are mssed by radologsts[1][2]. Computer-ad dagnoss (CAD) systems have been wdely used to help mprove the detecton precson[5][6]. Dagnoss nformaton of dgtal mammography need be saved losslessly n CAD. Wthout compresson, the sze of each dgtal mammogram can be more than 4 MB. Ths brngs a huge challenge to the current medcal system. Several lossless and lossy compresson method have been presented to resolve ths problem[7-18]. However, lossless compresson has brought about at most only 4:1 compresson rato. Most lossy compresson algorthms Ths work was supported by The Natonal Natural Scence Foundaton of Chna ( , , ), and The Natural Scence Foundaton of Zhejang Provnce (Y ) need take nto account the specal clncal ssue to be dealt wth[8-17]. Recever operaton characterstc(roc) analyss on lossy compresson show that t s promsng to use lossy technques n medcal mage compresson[8]. In recent years, dgtal mammography compresson has become a research focus n the feld of medcal mage processng. Prevous studes have evaluated, wth CAD systems or observer s performance studes, lossy compresson n dgtal mammography. Good and Zheng assessed the detecton of masses and clustered mcrocalcfcatons n JPEG mammograms by means of ROC study[9][10]. Kocss found that compressed mammograms wth wavelet transform algorthm at 40:1 compresson rato provded perceptually lossless compresson[11]. Perlmutter et al. found no sgnfcant dfferences between orgnal and compressed mages at 80:1 compresson rato of 57 dgtal mammograms compressed wth the set parttonng n herarchcal trees (SPIHT) algorthm[12]. Sung, Suryanarayanan and Penedo compared the performance of detecton of prmary sgns of breast cancer usng orgnal mages and mages reconstructed at 20:1 compresson rato after JPEG2000 compresson[13-15]. In a smlar study, Suryanarayanan et al. obtaned that JPEG2000 does not affect the CAD scheme for detectng masses, but detecton cluster of mcrocalcfcatons s affected wth compresson at 30:1 compresson rato[16]. Idrs found that false postve rate of mcrocalcfcatons was as hgh as 66 percent and some detals wth lessons gnored at 100:1 compresson rato after JPEG2000 compresson, whch may lead to unnecessary msdagnoss[17]. Penedo found that detecton of mcrocalcfcaton clusters and masses was not falure when segmented breast regon s compressed by JPEG2000 and OB-SPIHT (object-based set parttonng n herarchcal trees) at the compresson rato of 40:1 and 80:1[18]. In ths paper, a new dagnoss lossless compresson algorthm based on CAD technology and mutple arbtrary shape regon of nterests(s) codng framework s proposed for dgtal mammography to

2 34 A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on { D _ max} = 1LM R Ω M ISA-DWT Λ M Modfed SPIHT Ω dvded Ω M-1 ISA-DWT Λ M-1 R M-1 R 1 Modfed SPIHT Codng Stream Ω 1 ISA-DWT Λ 1 Modfed SPIHT Fg.1 Flow dagram of mutple arbtrary shape s codng framework hgh compresson rato whle mantanng the dagnoss nformaton losslessly. Ths paper s organzed wth Secton II presentng the proposed compresson method. Secton III provdes the expermental results. Secton IV draws the concluson. II. PROPOSED METHOD A. Multple, arbtrary shape s codng framework Mutple, arbtrary shape s codng framework s gven as shown n Fgure.1. The codng framework can be summarzed as follows: Frstly, the mage plane s parttoned nto multple s wth dfferent prortes whch are marked by a gray mask, n whch the hgher gray value of the regon, the hgher prorty of the s wll be; Secondly, dfferent s are transformed nto ndependent regon bt streams by the nteger-to-nteger shape adaptve dscrete wavelet transform(isa-dwt) [17] n accordance wth the order from hghest to lowest prorty respectvely; Lastly, s wth the same proprty are codng by the modfed SPIHT algorthm [18] to generate the codng stream under ther target dstorton constrant and the target bt rate. 1) Gray mask of s: The mage plane Ω s parttoned nto N subsets, = 1, L, N.The N dfferent s need to be encoded wth dfferent prortes. The set of N prortes can be expressed as N H = { P, P, P, L, P } (1) where The prorty P s the prorty assgned to the th. P assgned to the th determnates the relatve mportance of the regon n the entre mage. A hgher prorty results n earler solated bt stream. Because dfferent s may have the same prorty, M dfferent prortes are produced as M P < P < P < L < P (2) where P s the th prorty. A gray mask s used to show the prortes of dfferent s, n whch the larger gray value of the regon means the hgher prorty. So the mask regon of the background s black and those s wth the same prorty have the same gray value. 2) Estmaton of the qualty of the reconstructed s: Durng the encodng process, for each prorty, a separate dstorton constrant s employed D D (3) Where D and D _ max _ max are the dstorton and the maxmum allowable dstorton of the th prorty, respectvely. s wth the same prorty has the same dstorton constrans. s wth dfferent prortes are separately represented by ISA-DWT to produce ndependent regon bt streams. ISA-DWT maps the s wth the same prorty nto a set of transform coeffcents defned over Λ wth Λ Λ where Λ s the wavelet coeffcent plane correspondng to the conventonal wavelet representaton of mage plane Ω. Accordng to Parseval s theorem of energy preservaton, energy n the mage doman s equal to that n ts wavelet doman for the orthonormal wavelet transform, and almost equal for the borthogonal wavelet transform. So the energy n the mage doman can be estmated by that n ts wavelet doman. We use orthonormal (or borthogonal) wavelet bass whch yelds: D Λ D D Λ (4) where s the dstorton of s, whch can be measured by parameters such as MSE, wth the th prorty measured n the wavelet doman. Therefore, we can use orthonormal (or borthogonal) wavelet bass to obtan the estmated qualty of the reconstructed by measurng the regon dstorton wthout addton computatonal cost of obtanng the reconstructed mage. The wavelet coeffcents are encoded by the modfed SPITH wth an addtonal task for the encoder to get the reconstructed wavelet coeffcents. For the value of n when a coordnate s moved to the LSP, t s known that n n+ 1 2 c [, < 2, where c [ ] s the wavelet

3 A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on 35 CAD Mutple, arbtraty shape s codng framework Extractng breast Breast regon Lossless compresson codng stream Dagnoss lossless compressed mammogram Orgnal mammogram Extractng pectoral muscle Pectoral muscle regon Lossy compresson codng stream Prorty mask Background Full lossless / lossy compressed mammogram Fg.2 Flow dagram of the proposed compresson scheme coeffcent of the orgnal mage. Plus the sgn bt and set the estmated reconstructed n wavelet coeffcent cˆ [, = ± 1.5 2, where c ˆ[ ] s the estmated wavelet coeffcent of reconstructed mage. Durng the refnement pass, c ˆ[, s added or subtracted by 2 n 1 when t nputs the bts of the bnary representaton of c [,. 3) Arbtarary shape lossy and lossless compresson algorthm A gray mask s nduced to mark the prortes of s. The prortes decde the order of the processng for coeffcents of the s at the encoder and the decoder. Those s wth the same prorty are transformed as one. Because of ts good codng performance for lossy and lossless compresson of arbtrary shape regon, the ISA-DWT s used to transform arbtarary shape nto ndependent regon bt streams. Then the modfed SPIHT algorthm s used to codng each regon to generate codng stream under ts target dstorton constrant and the target bt rate. So each can acheve ts target reconstructed qualty by estmatng the dstorton of ts wavelet coeffcents n the encoder. B. The proposed dagnoss loseless compresson method for dgtal mammography The dagram of the proposed compresson scheme, whch manly ncludes CAD and multple arbtrary s codng framework, s shown n Fg.2. The orgnal mammogram s dvded nto breast regon, pectoral muscle and background by the CAD technology. CAD extracts breast at frst and then pectoral muscle to get the prorty mask n whch regon of hgher prorty s marked wth hgher gray value. Then mutple arbtrary shape s codng framework s used to codng all s. Specfcally, the breast regon s compressed losslessly by arbtrary shape compresson algorthm; the pectoral muscle regon s compressed lossly under gven dstorton constrant; the background can be dscarded or compressed lossly as user s wll. C. Extractng the breast regon The breast s extracted by a four-step method[21] as Fg.3 shows. Step 1: Iteratve thresholdng s used to seperate breast from the background and lne scan to locate the breast; Step 2: Column scan and utlzng the least square estmaton(lse) to detect the orentaton of horzontal frame; Step 3: Elastc thread technque s appled to cut the conglutnaton of the breast and the frame; Step 4: Watershed method s used to refne the breast boundary; D. Segmentng the pectoral muscle A model-based algorthm[22] s used to segment the pectoral muscle from the breast as Fg.4 shows. Two contour lne models are set up to descrbe the ntensty dstrbuton of surroundng area of pectoral muscle. After the medlateral oblque(mlo) of each mammogram s adjusted to make pectoral muscle at the same poston such as the lower rght corner, a seres of s wth dfferent szes are appled upon the regon surroundng the pectoral muscle to obtan the optmal threshold and mean square error (MSE) curve. The model type of current pectoral muscle regon can be determned accordng to MSE curve shape, and the optmal threshold of the pectoral muscle boundary be computed by ts model s character. After thresholdng, the boundary of man threshold regon s extracted, then zonal hough transform s used to approach the edge of the pectoral muscle n optmal. The maxmum gradent of the optmal can be got by lne scan and the second fttng lne be used to form a two-stage fttng lne by zonal hough transform. At last, elastc thread and polygon approachng technques are carred out to refne

4 36 A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on Mammogram D < D max (5) Where D s the reconstructed dstorton n wavelet doman and D max s the target one. Usng teratve thresholdng to get the background threshold Separate the background regon Lne scan to locate the breast Column scan and usng LSE to detect the orentaton of horzontal frame Usng elastc lne technology to segment lateral border Usng watershed method to segment the breast regon Usng morphologcal open operaton to refne edges Fg.3 Flow dagram of extractng the breast regon ts boundary. The ntensty dstrbuton of breast and pectoral muscle s qute dfferent for dfferent mammograms. Two models wth dfferent characters are used to avod the dffcultes of segmentaton pectoral muscle as the tradtonal approach meets. E. compresson algorthm for breast regon and pectoral muscle Because breast regon contans mportant dagnoss nformaton, t needs to be compressed losslessly. Breast regon s transformed by ISA-DWT, and then encoded by the modfed SPIHT to produce the ultmate codng stream, as Fg.5 shows. For pectoral muscle regon, t s not qute possble that there s dagnoss nformaton n t. So t can be compressed lossy wth relatvely hgh compresson rato, as Fg.6 shows. It subjects to the followng restrct condton: Fg.4 Flow dagram of segmentng the pectoral muscle

5 A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on 37 Fgure 5. Flow dagram of codng of breast regon D max Fg.7 The orgnal mammogram Fg.6 Flow dagram of codng of pectoral muscle III. Expermental results A set of mammograms are tested wth the proposed method. Fg.7 shows the orgnal mammogram of pxels at 8 bt. After extractng the breast and pectoral muscle, the mask s obtaned to show dfferent regons, n whch larger gray value means hgher prorty as Fg.8 shows. The nteger-to-nteger 5/3 and 9/7-F wavelet are used n ISA-DWT. The dstorton s evaluated by means of PSNR n wavelet doman. The target PSNR s set as 36dB and the background s dscarded completely. Table I gves the compresson rato comparson for dfferent compresson methods, where lossless compresson s adopted n JPEG2000. It s evdent that nteger-to-nteger 5/3 and 9/7-F wavelet can acheve better compresson effcency than JPEG2000 and 5/3 wavelet shows the best one. Fg.9 shows the reconstructed mammogram for nteger-to-nteger 5/3 wavelet. The wth lessons marked n Fg.6 s magnfed n Fg.10(a). The reconstructed regon wth nteger-to-nteger 5/3 wavelet shows completely the same effect as the orgnal one n Fg.10, whch means that the proposed method can acheve qute good compresson performance whle not brngng any dagnoss nformaton loss. Fg.8 mask after extractng the breast and pectoral muscle TABLE I. COMPRESSION RATIO COMPARISON JPEG2000 IWT-5/3 IWT-9/7-F Compresson rato

6 38 A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on Fg.9 the reconstructed mammogram (a) The orgnal regon (b) the reconstructed regon Fg.10 contanng lessons IV. CONCLUSION In ths paper, the CAD technology and multple arbtrary shape s compresson framework are combned to realze dagnoss lossless compresson for dgtal mammography. Prmary expermental results show qute good compresson effcency whle keepng the dagnoss nformaton lossless, especally for nteger-to-nteger 5/3 wavelet. Motvated by the results obtaned here, our next study s gong to carry out the clncal evaluaton of the proposed method for dgtal mammography. REFERENCES [1] R. G. Brd, T. W. Wallace, and B. C. Yankaskas, Analyss of cancers mssed at screenng mammography, Radology, vol. 184, pp ,1992. [2] H. Burhenne, L. Burhenne, F. Goldberg, T. Hslop, A. J. Worth, P. M.Rebbeck, and L. Kan, Interval breast cancers n the screenng mammographyprogram of Brtsh Columba: Analyss and classfcaton, Am. J. Roentgenol., vol. 162, pp , [3] K. Do, H. MacMahon, S. Katsuragawa, R. M. Nshkawa, and Y. Jang, Computer-aded dagnoss n radology: Potental and ptfall, Eur. J.Radol., vol. 31, pp , [4] H. L, K. J. Lu, and S. Lo, Fractal modelng and segmentaton for the enhancement of mcrocalcfcatons n dgtal mammograms, IEEE Trans. Med. Imag., vol. 16, no. 6, pp , Dec [5] Bradley J. Erckson M D, Irreversble Compresson of Medcal Images, J. Dgt. Imag., 15(1): 5-14,2002. [6] Ishgak T, Sakuma S, Ikeda M et al, Clncal evaluaton of rreversble mage compresson: Analyss of chest magng wth computed radography, J. Radol., 175: , [7] Slone R M, Foos D H, Whtng B R et al, Assessment of vsually lossless rreversble mage compresson: Comparson of three methods by usng an mage comparson workstaton, Radology-Computer Applcatons, 215(2): , [8] Penedo M, Pearlman W A, Tahoces P G et al, Regon-based wavelet codng methods for dgtal mammography. IEEE Trans Med Imagng, 22: , [9] Good W F, Sumkn J H, Ganott M et al, Detecton of masses and clustered mcrocalcfcatons on data compressed mammograms: an observer performance study. AJR Am J Roentgenol, 175: , [10] Zheng B, Sumkn J H, Good W F et al, Applyng computer-asssted detecton schemes to dgtzed mammograms after JPEG data compresson: an assessment. Acad Radol, 7: , [11] Kocss O, Costardou L, Varak L et al, Vsually lossless threshold determnaton for mcrocalcfcaton detecton n wavelet compressed mammograms. Eur Radol, 13: , [12] Perlmutter S, Cosman P, Gray Ret al, Image qualty n lossy compressed dgtal mammograms. Sgnal Process,59: ,1997. [13] Sung M M, Km H J, Km E K et al, Clncal evaluaton of JPEG2000 compresson for dgtal mammography. IEEE Trans Nucl Sc, 49: , [14] Suryanarayanan S, Karellas A, Vedantham S et al, A perceptual evaluaton of JPEG 2000 mage compresson for dgtal mammography: contrast detal characterstcs. J Dgt Imagng, 17:64 70, [15] Penedo M, Carrera J M, Tahoces P G et al, Effects of JPEG2000 data compresson on an automated system for detectng clustered mcrocalcfcatons n dgtal mammograms, IEEE Trans Inf Technol Bomed, 10(2): , [16] Suryanarayanan S, Karellas A, Vedantham S et al, Detecton of Smulated Lesons on Data compressed Dgtal Mammograms, Radology, 236:31 36, [17] Idrs F M, AlZubad N I, Detecton of breast cancer n the JPEG2000 doman, Trans Eng, Comput Technol, 8: , [18] Penedo M, Souto M, Tahoces P G et al, FROC evaluaton of JPEG2000 and object-based SPIHT lossy compresson on dgtzed mammograms, Radology, 237: , [19] Ahmed Abu-Hajar and Rav Sankar, Integer-to-nteger shape adaptve wavelet transform for regon of nterest mage codng, Dgtal Sgnal Processng Workshop, pp:94 97, Oct [20] Zhongmn Lu et al., Cascaded dfferental and wavelet compresson of chromosome mages, IEEE Transacton on Bomedcal Engneerng, 49(4),pp , Apr

7 A New Dagnoss Loseless Compresson Method for Dgtal Mammography Based on 39 [21] Xu W, Study on computer-aded dagnoss of ammograms, Ph.D. dssertaton, Dept. Bomedcal Engneerng, Zhejang Unv, Hangzhou, Chna, (n Chnese) [22] Xu W, Xa S, A model based algorthm to segment the pectoral muscle n mammograms, n proc IEEE Int. Neural Networks & Sgnal Processng Conf, pp , Dec Png Xu was born n He receved the Ph.D degree n control theory and control engneerng from Zhejang Unversty, Hangzhou, Chna, n He has been an Assocate professor of College of Lfe Informaton Scence & Instrument Engneerng of Hangzhou Danz Unversty snce Hs current research nterest nclude mage compresson, compressve sensng, and bomedcal mage processng. Yan Zuo was born n She receved the Ph.D degree n control theory and control engneerng from Shangha Jao Tong Unversty, Shangha, Chna, n She has been an Assocate professor of College of automaton of Hangzhou Danz Unversty snce We-Dong Xu was born n He receved the Ph.D degree n bomedcal engneerng from Zhejang Unversty, Hangzhou, Chna, n He has been an Assocate professor of College of Lfe Informaton Scence & Instrument Engneerng of Hangzhou Danz Unversty snce Hs current research nterest nclude computer-aded dagnoss, and bomedcal mage processng. Hua-Je Chen was born n He receved the Ph.D degree n control theory and control engneerng from Zhejang Unversty, Hangzhou, Chna, n He has been an Assocate professor of College of Lfe Informaton Scence & Instrument Engneerng of Hangzhou Danz Unversty snce Hs current research nterest nclude mage processng and computer vson.