Using Logistic Regression to Distinguish Between Fatty and Fibroid Masses in Medical Imaging (Ultrasound Image)

Keywords: logistic regression, Medical imaging (Ultrasound image), ROI, Classification


      Medical image analysis has great significance in the field of medicine, especially in non-invasive and clinical studies. Medical imaging techniques and it analysis tools enable the physicians and Radiologists to reach at a specific diagnosis. In this study has been studying the link between (statistical model ,Computer vision and medical images) using the application binary logistic model to analyze medical imaging (Ultrasound image for breast), for distinguishing  between shape of mass (Fatty & Fibroid) through selecting regions of interest  (ROI) of the mass, and by extracting statistical and geometric measurements ( Mean, Standard Deviation, Circle, Solidity,…..), best logistic model was able to be estimated which is composed of significance parameters (Integrated Density, Median, Skew, Kurt), Then, It was reached a good percentage of the classification and distinction between (Fibroid) and (Fatty) masses (85.7% vs. 82.9%) with the total percentage of classification equal to 84.4%.


Abdul Razzaq, H. (2004) the following Measurements Distinguish Malignant Calcifications of Benign Calcifications in Breast Images Digitized. Journal of Science and Engineering, Damascus University, Syria, No. 20, pp. 209-225
AL-Ghrabi, H. (2001) Quantum Analysis of Noise in Photonic system. M.Sc., Thesis, physics Dept., College of Education for Girls, Baghdad University, Iraq
Al-Naami, B., Bashir, A., Amasha, H., Al-Nabulsi, J. & Majeed AL malty, A. (2009) Statistical Approach for Brain Cancer Classification Using a Region Growing Threshold. Springer Science + Business Media, LLC. DOI 10.1007/s10916-009-9382-6.
Al-Samaraie, M. F., Al Saiyd, N. A. M.,(2008) "Medical Colored Image Enhancement using Wavelet Transform Followed by Image Sharpening," Ubiquitous Comput. Commun. J, vol. 6, pp. 1-8, 2008
Bagley, C., White, H. & Golomb, A. (2001) Logistic Regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain. Journal of Clinical Epidemiology, Vol. 54, Issue 10, pp. 979–985.
DAVID, W. & HOSMER, JR. (2013) Applied Logistic Regression. Third Edition, John Wiley & Sons, Inc., Hoboken, New Jersey, Canada.
Ferreira, T. & Rasband, W. (2012) ImageJ User Guide. ImageJ/Fiji 1.46.
Gonzales, C. & Woods, E. (2008) Digital Image Processing. Third Edition, Prentice-Hall Publishing, New Jersey, United States of America.
Gonzalez, R.C. & Wintz, P. (1992) Digital Image Processing. Third Edition, Addision wesly.
Jiang .Y, Nishikawa .RM, Wolverton .DE, Metz .CE, Giger .ML, Schmidt .RA, Vyborny .CJ & Doi .K (1996) Malignant and benign clustered micro calcifications: Automated Feature analysis and classification. Radiology, Vol. 198, Issue 3, pp. 671–678.
Kadhim .A (2012) Development algorithm- Computer Program of Digital mammograms Segmentation for detection of masses breast using Marker Controlled Watershed in MATLAB environment. Journal of kerbala university, Vol. 1, PP. 114-123.
Krishna, K.S. & Akansha S. (2010) a study of image segmentation a logarithms for different types of Image, International Journal of Computer science, Vol. 7, Issue 5, pp. 414-417
Rajaa, F. (2015) Computer Aided Diagnosis of Breast Cancer Using Image Processing Techniques and Neural Networks with Logistic Regression. Master Thesis Proposal Submitted In Partial Fulfillment of the Requirement of the Master Degree in Computer Science, Middle East University, Amman, Jordan.
Saritha, C., Devi, B., Mohammed, I., Sekhar, C., Anil, S. & Rani, H. (2016) Giant lipoma of breast: A rare case report, Iaim Journal International Archives of Integrated Medicine, Vol. 3, Issue 1, pp. 116-118.
Williams, S., Bulstrode, J. & O’Connell, P. (2013) Bailey & Love’s Short Practice of Surgery, Twenty sixth Edition , CRC Press, U K.
Yarandi, H. & Simpson, S. (1991) the logistic regression model and the odds of testing HIV Positive. Nursing Research, Vol. 40, Issue 6, pp. 372-373.
Yusuff, H., Mohamad, N., Ngah, U. &Yahaya, A. (2012) Breast cancer analysis using logistic regression. IJRRAS, Vol. 10, Issue 1, PP. 14-22.
How to Cite
“Using Logistic Regression to Distinguish Between Fatty and Fibroid Masses in Medical Imaging (Ultrasound Image)”. ZANCO Journal of Pure and Applied Sciences, Vol. 28, no. 5, Nov. 2016, pp. 193-01, doi:10.21271/zjpas.v28i5.730.