Automatic Detection of Mass Type Breast Cancer using Texture Analysis in Korean Digital Mammography
In this study, we present an advanced detection
technique for mass type breast cancer based on texture information
of organs. The proposed method detects the cancer areas in three
stages. In the first stage, the midpoints of mass area are determined
based on AHE (Adaptive Histogram Equalization). In the second
stage, we set the threshold coefficient of homogeneity by using
MLE (Maximum Likelihood Estimation) to compute the uniformity
of texture. Finally, mass type cancer tissues are extracted from the
original image. As a result, it was observed that the proposed
method shows an improved detection performance on dense breast
tissues of Korean women compared with the existing methods. It is
expected that the proposed method may provide additional
diagnostic information for detection of mass-type breast cancer.
[1] L. Zhan, Y. Ren, C. Huang, F. Liu, "A novel automatic tumor detection
for breast cancer ultrasound Images," Eighth International Conference
on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 4, pp.
401-404, July. 2011.
[2] V. Nadvoretskiy, S. Ermilov, "Image processing and analysis in a
dual-modality optoacousitc/ultrasonic system for breast cancer
diagnosis" 2011 Society of Photo-Optical Instrumentation Engineers
(SPIE), vol. 12, pp. 7899-08, Jan. 2011.
[3] B. Liu, H.D. Cheng, J. Huang, J. Tian, X. Tang, J. Liu, "Fully
automatic and segmentation-robust classification of breast tumors
based on local texture analysis of ultrasound images", Pattern
Recognit. vol. 43, pp.280-298, Jan. 2010.
[4] N. Karssemeijer, "Detection of stellate distortions in Mammograms,"
IEEE Trans. Med. Imaging, vol. 15, pp. 611-619, Oct. 1996
[5] M. Kevin, W. Kelly, Judy Dean, L. Sung-Jaee, "Breast cancer detectio
n using automated whole breast ultrasound and mammography in radi
ographically dense breasts" Eur. Radiol, vol. 20, pp. 734-742, 2010.
[6] A. I. Phipps, C. I. Li, K. Kerlikowske, W. E. Barlow, "Risk factors for
ductal, lobular, and mixed ductal-lobular breast cancer in a screening
population." Cancer Epidemiol. Biomarkers Prev. vol. 19, pp.
1643-1654, May. 2010.
[7] K. Ghosh, K. R. Brandt, C. Reynolds, CG Scott, "Tissue composition
of mammographically dense and non-dense breast tissue" Breast Canc
er Res. Treat, vol. 131 pp. 267-275, 2012.
[8] J. Ding , R. Warren, A. Girling, D. Thompson, "Mammographic Densi
ty, Estrogen Receptor Status and Other Breast Cancer Tumor Characte
ristics". Breast J, vol. 16, pp. 279-289, Jun. 2010.
[9] M. Guido, T. Brake, N. Karssemeijer, "Single and multiscale detection
of masses in digital mammograms", IEEE Trans. Medical Imaging, v
ol. 18, no. 7, pp-628-639, July. 1999.
[10] H. Sheng Fang, C. Ruey Feng, C. Dar Ren, D. Chen, "Characterization
of speculation on ultrasound lesions." IEEE Trans. Medical Imaging,
vol. 18, no. 7, pp. 628-639, July. 1999.
[11] N. R. Mudigonda, R. M Rangayyan, J. E Leo Desautels, "Detection of
Breast Masses in Mammograms by Density Slicing and Texture Flow-
Field Analysis", IEEE Trans.Med. Imag, vol. 20, no. 12,
pp. 1215-1227, Dec. 2001.
[12] Q. Xue, Y. Zhuangzhi, W. Shengqian, "Mammographic Feature Enha
ncement Based on Second generation Curvelet Transform" 2010 3rd In
ternational Conference on Biomedical Engineering and Informatics(B
MEI 2010), vol. 7, pp. 349-353, Oct. 2010
[13] F. Moayedi, Z. Azimifar, R. Boostani, "Contourlet-based
mammography mass classification using the SVM family" Comput.
Biol. Med, vol. 40, pp. 373-383, April. 2010.
[14] S. Rathore, M. A Liftikhar, M. Hussain, "Texture Analysis for Liver
Segmentation and Classification: A Survey" 2011 9th Frontiers of
Information Technology(FIT), vol. 9, pp. 121-126, 2011.
[15] S. Don, E. Choi, M.Dugki, "Breast Mass Segmentation in Digital
Mammography Using Graph Cuts" Communication in computer and
information science, vol. 206, pp. 88-96, 2011.
[16] H. D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, Ling Zhang, "Automat
ed breast cancer detection and classification using ultrasound images:
A survey". Pattern Recognit, vol. 43, pp. 299-317, Jan. 2010.
[17] G. Karbani, J. N. Lim, J. Hewison, "Culture, Attitude and Knowledge
about Breast Cancer and Preventive Measurers: a Qualitative Study of
South Asian Breast Cancer Pationts in the UK" Asian Pac J Cancer
Prev, vol. 12, pp. 1619-26, 2011.
[18] H. D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, Ling Zhang, "Automat
ed breast cancer detection and classification using ultrasound images:
A survey". Pattern Recognit. vol. 43, pp. 299-317, Jan. 2010.
[19] M. Khalid, R. Yusof, "A Comparative Study of Feature Extraction
Methods for Woo Texture Classification" 2010 Sixth International
Conference on Signal-Image Technology and Internet Based System,
vol. 17, pp. 23-29, 2010.
[20] S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique for
digital communications channel equalization using radial basis
function networks," IEEE Trans. Neural Networks, vol. 4, pp.
570-578, July 1993.
[1] L. Zhan, Y. Ren, C. Huang, F. Liu, "A novel automatic tumor detection
for breast cancer ultrasound Images," Eighth International Conference
on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 4, pp.
401-404, July. 2011.
[2] V. Nadvoretskiy, S. Ermilov, "Image processing and analysis in a
dual-modality optoacousitc/ultrasonic system for breast cancer
diagnosis" 2011 Society of Photo-Optical Instrumentation Engineers
(SPIE), vol. 12, pp. 7899-08, Jan. 2011.
[3] B. Liu, H.D. Cheng, J. Huang, J. Tian, X. Tang, J. Liu, "Fully
automatic and segmentation-robust classification of breast tumors
based on local texture analysis of ultrasound images", Pattern
Recognit. vol. 43, pp.280-298, Jan. 2010.
[4] N. Karssemeijer, "Detection of stellate distortions in Mammograms,"
IEEE Trans. Med. Imaging, vol. 15, pp. 611-619, Oct. 1996
[5] M. Kevin, W. Kelly, Judy Dean, L. Sung-Jaee, "Breast cancer detectio
n using automated whole breast ultrasound and mammography in radi
ographically dense breasts" Eur. Radiol, vol. 20, pp. 734-742, 2010.
[6] A. I. Phipps, C. I. Li, K. Kerlikowske, W. E. Barlow, "Risk factors for
ductal, lobular, and mixed ductal-lobular breast cancer in a screening
population." Cancer Epidemiol. Biomarkers Prev. vol. 19, pp.
1643-1654, May. 2010.
[7] K. Ghosh, K. R. Brandt, C. Reynolds, CG Scott, "Tissue composition
of mammographically dense and non-dense breast tissue" Breast Canc
er Res. Treat, vol. 131 pp. 267-275, 2012.
[8] J. Ding , R. Warren, A. Girling, D. Thompson, "Mammographic Densi
ty, Estrogen Receptor Status and Other Breast Cancer Tumor Characte
ristics". Breast J, vol. 16, pp. 279-289, Jun. 2010.
[9] M. Guido, T. Brake, N. Karssemeijer, "Single and multiscale detection
of masses in digital mammograms", IEEE Trans. Medical Imaging, v
ol. 18, no. 7, pp-628-639, July. 1999.
[10] H. Sheng Fang, C. Ruey Feng, C. Dar Ren, D. Chen, "Characterization
of speculation on ultrasound lesions." IEEE Trans. Medical Imaging,
vol. 18, no. 7, pp. 628-639, July. 1999.
[11] N. R. Mudigonda, R. M Rangayyan, J. E Leo Desautels, "Detection of
Breast Masses in Mammograms by Density Slicing and Texture Flow-
Field Analysis", IEEE Trans.Med. Imag, vol. 20, no. 12,
pp. 1215-1227, Dec. 2001.
[12] Q. Xue, Y. Zhuangzhi, W. Shengqian, "Mammographic Feature Enha
ncement Based on Second generation Curvelet Transform" 2010 3rd In
ternational Conference on Biomedical Engineering and Informatics(B
MEI 2010), vol. 7, pp. 349-353, Oct. 2010
[13] F. Moayedi, Z. Azimifar, R. Boostani, "Contourlet-based
mammography mass classification using the SVM family" Comput.
Biol. Med, vol. 40, pp. 373-383, April. 2010.
[14] S. Rathore, M. A Liftikhar, M. Hussain, "Texture Analysis for Liver
Segmentation and Classification: A Survey" 2011 9th Frontiers of
Information Technology(FIT), vol. 9, pp. 121-126, 2011.
[15] S. Don, E. Choi, M.Dugki, "Breast Mass Segmentation in Digital
Mammography Using Graph Cuts" Communication in computer and
information science, vol. 206, pp. 88-96, 2011.
[16] H. D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, Ling Zhang, "Automat
ed breast cancer detection and classification using ultrasound images:
A survey". Pattern Recognit, vol. 43, pp. 299-317, Jan. 2010.
[17] G. Karbani, J. N. Lim, J. Hewison, "Culture, Attitude and Knowledge
about Breast Cancer and Preventive Measurers: a Qualitative Study of
South Asian Breast Cancer Pationts in the UK" Asian Pac J Cancer
Prev, vol. 12, pp. 1619-26, 2011.
[18] H. D. Cheng, Juan Shan, Wen Ju, Yanhui Guo, Ling Zhang, "Automat
ed breast cancer detection and classification using ultrasound images:
A survey". Pattern Recognit. vol. 43, pp. 299-317, Jan. 2010.
[19] M. Khalid, R. Yusof, "A Comparative Study of Feature Extraction
Methods for Woo Texture Classification" 2010 Sixth International
Conference on Signal-Image Technology and Internet Based System,
vol. 17, pp. 23-29, 2010.
[20] S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique for
digital communications channel equalization using radial basis
function networks," IEEE Trans. Neural Networks, vol. 4, pp.
570-578, July 1993.
@article{"International Journal of Medical, Medicine and Health Sciences:50321", author = "E. B. Jo and J. H. Lee and J. Y. Park and S. M. Kim", title = "Automatic Detection of Mass Type Breast Cancer using Texture Analysis in Korean Digital Mammography", abstract = "In this study, we present an advanced detection
technique for mass type breast cancer based on texture information
of organs. The proposed method detects the cancer areas in three
stages. In the first stage, the midpoints of mass area are determined
based on AHE (Adaptive Histogram Equalization). In the second
stage, we set the threshold coefficient of homogeneity by using
MLE (Maximum Likelihood Estimation) to compute the uniformity
of texture. Finally, mass type cancer tissues are extracted from the
original image. As a result, it was observed that the proposed
method shows an improved detection performance on dense breast
tissues of Korean women compared with the existing methods. It is
expected that the proposed method may provide additional
diagnostic information for detection of mass-type breast cancer.", keywords = "Mass Type Breast Cancer, Mammography,
Maximum Likelihood Estimation (MLE), Ranklets, SVM", volume = "6", number = "4", pages = "62-5", }
