جۆری توێژینه‌وه‌: Original Article

نوسه‌ران

1 Sulaimani Polytechnic University, Kurdistan Region, Iraq

2 Sulaimani University, College of Informatics, IT Department, Kurdistan Region, Iraq

3 3 Sulaimani Polytechnic University, Kurdistan Region, Iraq

پوخته‌

This research work, presents a computer-aided mammography detection of mass
image for Malignant breast cancer a system has been developed to help radiologists
in order to increase diagnostic accuracy and called (ImageCBR). The aim of this
work to find or detect similar Malignant image mass of breast cancer from base
knowledge by given a target one. similarity Generally, a ImageCBR system consists
of four stages: (a) preprocessing of the image (b) segmentation of regions of interest,
such as a well-known mass breast features extraction and selection (shape, size,
density, margin), and finally (c) image similarity (target and source). The
performance evaluation metrics of ImageCBR systems are also reviewed.

وشه‌ بنچینه‌ییه‌كان

[1] what_is_bc: breast cancer. (2015, October 23). Retrieved July 21, 2016, from
breast cancer Web site : http://www.breastcancer.org
[2] G. Kamp, S. Lange, C. Globig,. (1998). Case-based Reasoning
Technology:related area. Case-based Reasoning Technology:from Foundations to
Application,. Berlin: Springer.
[3] Jimmy Singla, Dinesh Grover, Abhinav Bhandari. (May 2014) “Medical Expert
Systems for Diagnosis of Various Diseases”International Journal of Computer
Applications (0975 – 8887)Volume 93 – No.7.
[2] Mohamed Hachama, Agn`es Desolneux and Fr´ed´eric J.P. Richard” A Bayesian
Technique for Image Classifying Registration” (2012). IEEE TRANSACTIONS ON
IMAGE PROCESSING, TIP-08569-2012.R1, MAI.
[5] (Online) (2014). “The mini-MIAS database of mammograms. (2012, December
11). Retrieved September 1, 2014, from Mammographic Image Analysis Society”
: http://www.mammoimage.org
[6] Kovalerchuck, B, Triantaphyllou, E., Ruiz, J. F., & Clayton, J. (1997). Fuzzy
logic in computer-aided breast-cancer diagnosis: Analysis of lobulation. Artificial
Intelligence in Medicine, 75-85.
[7] Watson I. (1999). “Case-based reasoning is a methodology not a technology”
Knowledge-Based Systems, 303–308.
[8] S.Singh, A. M. (2000). Indentification of regions of intersest in Digital
mammograms. Journal of Intelligent Systems.
[9] Jalalian A1, M. S. (2013). Computer-aided detection/diagnosis of breast cancer in
mammography and ultrasound Clin Imaging. Clin Imaging., 430-6.
[10]Y. K. Eugene and R.G. Johnston. (1996). The Ineffectiveness of the Correlation
Coefficient for Image Comparisons”. Los Alamos: Technical Report LA.
[11] Rodgers J. L. and Nicewander W. A. (1988). “Thirteen Ways to Look at the
Correlation Coefficient”. The American Statistician, 42.

[12] Eugene K . Jen, Roger G.Johnston. (n.d.). Nov.2016) “The Ineffectiveness of
Correlation Coefficient for Image Comparisons”. New Mexico: Research Paper
prepared by Vulnerability Assessment. Edition. 59-66;
[13] Singh R. and Shaw D. (2016). Experimental Analysis of Impact of Noise on
Various Edge Detection Techniques. World Congress on Engineering. London:
WCE.
[14]Salem Saleh Al-amri, Dr. N.V. Kalyankar and Dr. Khamitkar S.D . (2010).
IMAGE SEGMENTATION BY USING EDGE DETECTION. International Journal
on Computer Science and Engineering, 804-807.