Published on

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 Case study of CAD Based Mammographic Lesions using Wavelet Decomposition Elayabharathi.T1, Dr.Nagappan.A2 1 is a research Scholar in the Department of computer science &engineering at the Vinayaka mission research foundation deemed university, Salem, Tamilnadu, India.Abstract – This paper describes the efforts by study of the characteristics of true masses compared to the falsely detectedmasses is carried out using wavelet decomposition transform. According to the cancer statistics, the breast cancer incidence rateis increased almost every year since 1980, the death rate is shown a substantial decrease. Both trends may be attributed, in largepart to mammography which is widely recognized as the most sensitive technique for breast cancer detectionIndex terms –Lesions, Wavelet, contourlet, Mammogram, CAD1. Introduction difficulty in maximizing both sensitivity to tumoral Conventional mammography is a film based x-ray growths and specificity in identifying their nature.Technique referred as a screen-film mammography. Full X-ray mammography is the best current method for earlyfield digital mammography is a new technology in which a detection of breast cancer, with an accuracy of betweensolid state detector is used instead of film for the generation 85% and 95%[3]. Identifying abnormalities such asof the breast image. Modern applications, including calcifications and masses often requires the eye of acomputer aided detection and computer aided diagnosis, trained radiologist. As a result, some anomalies may becomputer display and interpretation, digital image and missed due to human error as a result of fatigue, etc.transmission and storage, require a digital format of the The development of CAD systems that assist themammogram radiologist has thus become of prime interest, the aim1.1 Objective being not to replace the radiologist but to offer a second The purpose of our study was to retrospectively opinion. Eventually, the state-of-the-art could advance toevaluate the impact on recall rates and cancer detection the point where such systems effectively substitute forwhen converting from film-screen to digital mammography trained radiologists, an eventuality that is desirable forin a small community-based radiology practice. small outfits that cannot afford to have an expertDigital mammography offers considerable advantages over radiologist at their continuous disposal. For example, afilm-screen mammography [1-3]. Despite advantages, it has CAD system could scan a mammogram and draw redbeen slow to be adopted. This reluctance is due to many circles around suspicious areas. Later, a radiologist canfactors, including the high initial capital expenditure and the examine these areas and determine whether they arequestion of whether the added expense results in a better true lesions or whether they are artifacts of the scanningmammography “product” [4-7]. The reluctance to upgrade process, such as shadows.to digital mammography is especially true of smallcommunity-based imaging centers, where capital is less To our knowledge, no prior study has compared cancerprevalent and patient volumes are lower than in larger detection and recall rates at a single center before and aftermetropolitan locations. the installation of a digital mammography system, keeping1.2 statistics and discussions the interpreting radiologists constant. Such a study would Breast cancer ranks first in the causes of cancer limit the number of uncontrolled variables, allowingdeaths among women and is second only to cervical potential outcomes to be mediated only by the introductioncancer in developing countries[8]. The best way to of the technology and the variability in the womenreduce death rates due to this disease is to treat it at an undergoing screening.early stage. Early diagnosis of breast cancer requires an 2. Backgroundeffective procedure to allow physicians to differentiate Considerable effort has been expended to developbetween benign tumors from malignant ones. CAD systems to aid the trained radiologist identifyDeveloping computer-aided diagnosis (CAD) systems to areas with possible pathology on an image. Most ofhelp with this task is a non-trivial problem, and current these efforts have concentrated on X-ray mammographymethods employed in pursuit of this goal illustrate the and chest radiography. A number of CAD schemesIssn 2250-3005(online) September| 2012 Pag1318
  2. 2. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5have been investigated in literature. These include: 3. Methods By careful consideration of the design of various• Subtraction techniques that identify anomalies by CAD schemes, it is possible to categorize the techniques comparison with normal tissue employed under three broad headings:• Topographic techniques that perform feature extraction and analysis to identify anomalies • Data reduction - the image is examined in order• Filtering techniques that use digital signal to identify the ROIs. processing filters, often developed especially to • Image enhancement - the ROIs are subjected to augment anomalies for easy detection processes that enhance or augment the visibility of• staged expert systems that perform rule-based pathological anomalies, such as microcalcifications analysis of image data in an attempt to provide a and lesions. correct diagnosis • Diagnosis - the ROIs are subjected to one or more The majority of CAD systems attempt to of the broad categories of procedures mentioned inidentify anomalies by either looking for image Section 2 in order to arrive at a diagnosis, mostdifferences based on comparison with known normal commonly in the form of “benign” or “malignant”tissue (subtraction techniques)[4] or by image featureidentification and extraction of features that correlate These categories are extremely broad, and therewith pathological anomalies, such as in texture analysis may exist CAD systems that subject images to(topographic techniques)[11, 4, 6, 36]. Most systems techniques that do not fall under one of them. However,proceed in stages, first examining the image data and most of the CAD systems employ methods that can beextracting pre-determined features, then localizing classified under one or more of them.regions of interest or ROIs which can be examinedfurther for potential anomalies. High degrees of 3.1 Data Reductionsensitivity have been achieved using several of these Data reduction is the process by which an imagetechniques, but many have been hampered by high is decomposed into a collection of regions that appear tofalse-positive rates and hence low specificity. The contain anomalies that differ from the surroundingproblem of false positives is compounded further by tissue. These regions are usually a strict subset of thethe fact that false positive rates are reported per original image and are subregions of the original imageimage, not per case. Since many radiological that may contain ROIs. By doing this, the CAD systemexaminations i n c l u d e more than one image, the actual need only process those subregions identified by the datanumber of false positives may be a multiple of those reduction step, rather than the entire input image. Datareported. reduction accomplishes two objectives A number of different approaches have been simultaneously[34]:employed in an effort to reduce false positive rates, manyof them focusing on the use of artificial neural • An increase in throughput v i a a reduction in inputnetworks (ANNs). A common metric used for evaluating datathe performance of CAD systems, the receiver operating • A reduction in false positives by limiting the scopecurve or ROC (see Appendix A), is commonly used to of the detection algorithms in the rest of the CADevaluate a CAD scheme’s degree of tradeoff between system to the ROIs only. With less of the originalsensitivity and specificity. The area under this curve, image to worry about, the CAD system gainsAz , is a measure of overall performance, with a value of specificity since less image means less false-Az closer to 1 indicating better performance. Since positives in general, assuming that the detection algorithms work as intended.sensitivity in most techniques is quite high, specificity It is clear that the most obvious way to performoften becomes the limiting factor, with techniques data reduction is to have a trained radiologist identifydisplaying higher specificity performing at higher Az the ROIs for the CAD system. This can bevalues. accomplished through a graphical interface to the CAD This study decomposes several techniques and system that allows the radiologist to specify suspiciousidentifies their salient features and characteristics w i t h regions. It should be noted that some CAD systems dorespect to performance. The extent of the array o f not require this step at all due to the nature of theirtechniques e x a m i n e d herein is by no means all- diagnostic process, such as that those that employinclusive; rather, a number of techniques are described subtraction techniques.and their performance evaluated.Issn 2250-3005(online) September| 2012 Pag1319
  3. 3. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 can then be used by a radiologist; however, the CAD scheme proposed in [2] uses pseudo-color mapping[10] to convert the grayscale to a color image. This is done since human vision can only discern a limited number of grayscale levels. The end results is a pseudo-color breast map in which the lesions have been highlighted in different colors and confirmed by visual inspection by a trained radiologist. Anguh et al[2] claim that this multiscale segmentation and enhancement method detects virtually all lesions identified by an expert radiologist in the process of visual inspection in initial tests on 25 mammograms. 4. Wavelet-Based Enhancement Koren et al[18] developed a contrast Figure 1: overview of front-end data reduction module with enhancement method based on the adaptation of images specific enhancement schemes for distinct mammographic features, which were then used to3.2 Image Enhancement combine the set of processed images into an enhanced Mammographic image enhancement methods are image. In their scheme, the mammo- graphic image istypically aimed at either improvement of the overall first processed for enhancement of microcalcifications,visibility of features or enhancement of a specific sign masses and stellate lesions. From the resultingof malignancy. Various schemes for doing this exist, enhanced image, the final enhanced image iswith most of them based in signal processing synthesized by means of image fusion[20]. Specifically,techniques used either in their original form (such as their algorithm consisted of two major steps:simple histogram equalization) or adapted for specificuse in mammography. 1. the image is first subjected to a redundant B-spline A number of generic image enhancement methods wavelet transform decomposition[18]exist. Histogram equalization and fuzzy image from which a set of wavelet coefficients is obtainedenhancement[35] are just two examples. Though a 2. the wavelet coefficients are modified distinctly forwhole slew of image enhancement techniques exist in the each type of malignancy (microcal- cifications,general domain, very few are specifically targeted at the stellate lesions or circumscribed masses).enhancement of mammographic images. Section 4 3. the multiple sets of coefficients thus obtained aredescribes one of them. fused into a single set from which the reconstruction3.3 Statistical Techniques is computed The algorithm is illustrated in Figure 2, as applied to a digitized mammogram that they Anguh et al[2] propose a multiscale method for obtained from the University of Florida database.segmenting and enhancing lesions of various sizes in The theoretical treatment for the mathematicsmammograms. The first stage applies a multiscale involved in this scheme is beyond the scope of thisautomatic threshold esti- mator based on histogram study. However, it is interesting to note that themoments to segment the mammogram at multilevels. enhance image produced by this scheme is “moreThe second stage then converts the segmented image easily interpreted by a radi- ologist compared tousing pseudo-color mapping to produce a color images produced via global enhancementimage[2]. The final result is analogous to a breast map techniques”[18]. It is yet to be seen whatwhich provides an adequate basis for radiological breast improvement this enhancement scheme cantissue differentiation and analysis in digital contribute t o existing CAD schemes.mammography. Their paper provides a treatment on themathematical theory of moments before present analgorithm for the multiscale thresholding of themammogram. The result of this thresholding techniqueis a mammographic map or breast map based onvarious thresholds with varying object sizes. This mapIssn 2250-3005(online) September| 2012 Pag1320
  4. 4. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 where 0 ≤ u ≤ 1. Figure 2. Overview of the image fusion algorithm based on B-Spline Wavelet Transform5. Classification Apply B´zier splines[17] to both lesion edetection and characterization, where lesion detectionis achieved by segmentation using a thresholdcomputed from the B´zier smoothed histogram and elesion characterization is achieved by means of fitnessbetween Gaussian and B´zier histograms of data eprojected on principal components of the segmented Figure 7. System overview of the B´zier spline-based elesions. The most interesting component of their thresholding and segmentation algorithm.systems in the use of the B´zier splines as a basis of ethresholding of the mammographic image - the overall 6. Resultsperformance of their classification scheme is significantly The database used for this work comes fromworse than that seen from, for example, the ANN-based vina yaka mis sio ns hospital and is a set of 43 digitalizedscheme used by Chen et al[2], mammographic images, 25 of them corresponding to benign B´zier splines are a spline approximation method, e pathologies and 18 to breast cancer. Due to the gooddeveloped by the French engineer Pierre B´zier for use e performance of the detection stage, only fewin the design of Renault automobile bodies. Since a microcalcifications are not detected. Detection of theB´zier curve lies within the convex hull of the control e maximum possible number of microcalcificactions is verypoints on which it is fitted, applying it to the important for the success of the system, being very criticalhistogram of the original image produces a smoothed the correct adjustment of noise thresholds in the contourlethistogram from which a threshold can be easily chosen pre-processing stage.by simply finding the largest minimum or therightmost inflection point, which is where the highest 7. Conclusionsbrightness level is located. As a rule, a B´zier curve e The proposed system combines several state of theis a polynomial of degree one less than the number of art image processing techniques, namely contourletcontrol points used. Since a typicalgrayscale image transforms for the noise removal of the mammographicconsists of 256 brightness levels, the histogram values of images and border detection with the wavelet transformthese levels can be used as the control points for a B´zier e modulus maxima lines. The tested wavelet basedcurve polynomial of degree 255. If the histogram levels compression method proved to be an accurate approachare denoted by pk = (xk , yk ), where both k and xk for digitized mammography.vary from 0 to 255, then these coordinatepoints can be blended to produce a position vector P(u) which describes the path of an approximating ReferencesB´zier polynomial between p 0 and p255 : e [1] Predrag Baki’c And. Application of neural networks in computer aided diagnosis of breast cancer. http://citeseer.nj.nec.com/404677.html. [2] M.M. Anguh and A.C. Silva. Multiscale segmentation and enhancement in mammo- grams.Issn 2250-3005(online) September| 2012 Pag1321
  5. 5. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 http://www.visgraf.impa.br/sibgrapi97/anais/ART03/A Statistics, 8(3), September 1999. RT03.ps.gz. [15] D. Hearn and M.P. Baker. Computer Graphics. [3] L. Baker. Breast cancer demonstration project: Prentice Hall, Inc., 2nd edition, 1994. five year summary report. Cancer, pages 194–225, [16] Y. Hirose, K. Yamashita, and S. Hijiva. 1982. Backpropagation algorithm which varies the [4] Keir Bovis and Sameer Singh. Detection number of hidden units. Neural Networks, 4:61– of masses in mammograms using texture features. 66, 1991. http://www.dcs.ex.ac.uk/research/pann/master/cameo/p [17] International Commission on Radiological Units aper80.ps. and Measurements. Medical Imaging - The [5] Garra B.S., Krasner B.H., Horii S.C., Archer S., Assessment of Image Quality, 1996. Muk S.K., and Zerman R.K. Improving the [18] Andrew Laine Iztok Koren and Fred Taylor. distinction between benign and malignant breast Enhancement via fusion of mammographic lesions: the value of sonographic texture analysis. features. http://citeseer.nj.nec.com/288745.htm. Ultrasound Imaging, 15:267–285, 1993. [19] Barry Kalman, Stan C. Kwasny, and [6] I. Christoyianni, E. Dermatas, and G. William R. Reinus. Kokkinakis. [20] I. Koren, A. Laine, and F. of ab- normal tissue in digital Neural classification Taylor. Image fusion [7] Shane Dickson. Investigation of the use of Neural using steerable dyadic wavelet transform. Networks for Computerised Medical Image Analysis. Proceedings of the IEEE International Conference PhD thesis, Department of Computer Science, on Image Processing, 3:232–235, 1995. University of Bristol, February 1998. [21] A. Laine, J. Fan, and W. Yang. Wavelets for [8] Chen D.R., Chang R.F., and Huang Y.L. contrast enhancement of digital mam- mography. Computer-aided diagnosis applied to us of solid IEEE Engineering in Medicine and Biology breast nodules by using neural networks. Magazine, 14(5):536–550, 1995. Radiology, 213(2):407–412, November http://citeseer.nj.nec.com/349213.html. 1999. http://www.cs.ccu.edu.tw/ [22] A. Laine, S. Schuler, J. Fan, and W. Huda. rfchang/radio9911.pdf. Mammographic feature enhancement by [9] V. Goldberg and A. Manduca. Improvement in multiscale analysis. IEEE Transactions in specificity of ultrasonography for diag- nosis of Medical Imaging, MI-13:725–740, 1994. breast tumors by means of artificial intelligence. http://citeseer.nj.nec.com/laine94mammographic.ht Medical Physics 1992, 19:1475– ml. 1481, 1992. [23] H. Li, K. Liu, and S. Lo. Fractal modeling and [10] R.C. Gonzalez and R.C. Woods. Digital Image segmentation for the enhancement of Processing. Addison Wesley., 1992. microcalcifications in digital mammograms. [11] R. Gupta and P.E. Undrill. The use of texture IEEE Trans. Med. Imaging, 16:785–798,1997. analysis to identify suspicious masses in http://citeseer.nj.nec.com/li97fractal.html. mammography. [24] S. Liu and E. Delp. Multiresolution detection http://www.biomed.abdn.ac.uk/Abstracts/A00337/ of stellate lesions in mammograms. In [12] R. Hanka, T. Harte, A. Dixon, D. Lomas, and P. Proceedings of the IEEE International Britton. Neural networks in the inter- pretation of Conference on Image Processing, pages 109–112, contrast-enhanced magnetic resonance images of October 1997. the breast. In Proceedings Current Perspectives in http://citeseer.nj.nec.com/article/liu97multiresolutio Healthcare Computing Conference, Harrogate, UK, n.html. pages 275– [25] Sheng Liu. Multiresolution detection of 283, March 1996. spiculated lesions in digital mammograms.[13] Trevor Hastie, Debra Ikeda, and Robert http://citeseer.nj.nec.com/179226.html. Tibshirani. Computer-aided diagnosis of mam- [26] Laura N. Mascio, Jose M. Hernandez, and mographic masses. Technical report, Clinton M. Logan. Departments of Preventive Medicine & Biostatis- [27] A. Mendez, P. Tahoces, M. Lado, M. Souto, and tics, University of Toronto, June 1996. J. Vidal. Computer aided diagnosis: Automatic ftp://utstat.toronto.edu/pub/tibs/mammo.ps.Z. detection of malignant masses in digitized[14] Trevor Hastie, Debra Ikeda, and Robert mammograms. Medical Physics, 25(6):957–964, Tibshirani. Statistical measures for the 1998. computer-aided diagnosis of mammographic [28] A. Ossen, T. Zamzow, H. Oswald, and E. Fleck. masses. Journal of Computational and Graphical Issn 2250-3005(online) September| 2012 Pag1322
  6. 6. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 Segmentation of medical images using neural- network classifiers. In Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED’94), pages 427– 432,1994. http://citeseer.nj.nec.com/ossen94segmentation.html[29] Riccardo Poli and Guido Valli. Optimum segmentation of medical images with hopfield neural networks. Technical Report CSRP-95-12, University of Birmingham School of Computer Science, October 1995. http://citeseer.nj.nec.com/156628.html.[30] Qi and Snyder. Lesion detection and characterization i n digital mammography by b´zier histograms. e http://citeseer.nj.nec.com/348097.html.[31] Gonzalez R.C. and Woods R.E. Image Compression, pages 312–315. Reading, Mass.: Wesley, 1991.[32] Guido Valli Riccardo. Neural networks and prior knowledge help the segmentation of medical images. http://citeseer.nj.nec.com/336457.html.[33] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back- propagating errors. Nature, 323:533–536, 1986.[34] H. Sari-Sarraf, S. S. Gleason, and R. M. Nishikawa. Front-end data reduction in computer-aided diagnosis of mammograms: A pilot study. In SPIE’s Medical Imag- ing Conference, February 1999. http://www- ismv.ic.ornl.gov/publications/spie99.pdf.[35] Sameer Singh and Reem Al-Mansoori. Identification of regions of interest in digital mammograms. Journal of Intelligent Systems, 10(2):183–217, 2000. http://www.dcs.ex.ac.uk/research/pann/pdf/pa nn SS 005.pdf.[36] P. Undrill and R. Gupta. Texture analysis and boundary refinement to outline mam- mography masses. IEEE Colloquium (Digest), 072:5/1–5/6, 1996.[37] Kevin S. Woods. Automated image analysis techniques for digital mammography. http://citeseer.nj.nec.com/woods94automated.html.Issn 2250-3005(online) September| 2012 Pag1323