Computer-Aided Analysis of Ultrasound Elasticity Images for Classification of Benign and Malignant Breast Masses

2010 ◽  
Vol 195 (6) ◽  
pp. 1460-1465 ◽  
Author(s):  
Woo Kyung Moon ◽  
Ji Won Choi ◽  
Nariya Cho ◽  
Sang Hee Park ◽  
Jung Min Chang ◽  
...  
Keyword(s):  
Breast Masses ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
Malignant Breast

scholarly journals Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

2018 ◽  
Vol 46 (9) ◽  
pp. 1419-1431 ◽  
Author(s):  
Gopichandh Danala ◽  
Bhavika Patel ◽  
Faranak Aghaei ◽  
Morteza Heidari ◽  
Jing Li ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Said Boumaraf ◽  
Xiabi Liu ◽  
Chokri Ferkous ◽  
Xiaohong Ma
Keyword(s):  
Feature Selection ◽  
Predictive Value ◽  
Breast Imaging ◽  
Feature Selection Method ◽  
Computer Aided Diagnosis ◽  
Screening Mammography ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

Analysis of temporal changes of mammographic features: Computer-aided classification of malignant and benign breast masses

2001 ◽  
Vol 28 (11) ◽  
pp. 2309-2317 ◽  
Author(s):  
Lubomir Hadjiiski ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Nicholas Petrick ◽  
Mark A. Helvie ◽  
...  
Keyword(s):  
Temporal Changes ◽  
Breast Masses ◽  
Computer Aided

Solid Breast Masses: Classification with Computer-aided Analysis of Continuous US Images Obtained with Probe Compression

Radiology ◽  
2005 ◽  
Vol 236 (2) ◽  
pp. 458-464 ◽  
Author(s):  
Woo Kyung Moon ◽  
Ruey-Feng Chang ◽  
Chii-Jen Chen ◽  
Dar-Ren Chen ◽  
Wei-Liang Chen
Keyword(s):  
Breast Masses ◽  
Computer Aided Analysis ◽  
Computer Aided

scholarly journals Computer-aided Classification of Breast Masses: Performance and Interobserver Variability of Expert Radiologists versus Residents

Radiology ◽  
2011 ◽  
Vol 258 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Swatee Singh ◽  
Jeff Maxwell ◽  
Jay A. Baker ◽  
Jennifer L. Nicholas ◽  
Joseph Y. Lo
Keyword(s):  
Interobserver Variability ◽  
Breast Masses ◽  
Computer Aided

scholarly journals Detection And Restoration of Cracked Digitized Paintings and Manuscripts Using Image Processing

2018 ◽  
Vol 7 (2.34) ◽  
pp. 39
Author(s):  
Nawafil Abdulwahab Farajalla Ali ◽  
Imad Fakhri Taha Al-Shaikhli ◽  
Raini Hasan
Keyword(s):  
Image Processing ◽  
Median Filter ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
Drying Up ◽  
Weighted Median ◽  
Mechanical Factors ◽  
Region Growth

Ancient paintings are cultural heritage that can be preserved via computer aided analysis and processing. These paintings deteriorate due to undesired cracks, which are caused by aging, drying up of painting material, and mechanical factors. These heritages need to be restored to their respective original or near-original states. There are different techniques and methodologies that can be used to conserve and restore the overall quality of these images. The main objective of this study is to analyze techniques and methodologies that have been developed for the detection, classification of small patterns, and restoration of cracks in digitized old painting and manuscripts. The purpose of the developed algorithm is to identify cracks using the thresholding operation, which was the output of the top-hat transform morphology. Afterwards, the breaks, which were wrongly identified as cracks, were separated for utilization in a semi-automatic procedure based on region growth. Finally, both the median filter and weighted median techniques were applied to fill the cracks and enhance image quality. 

Computer-aided classification of breast masses in ultrasonic B-scans using a multiparameter approach

2003 ◽  
Vol 50 (8) ◽  
pp. 1002-1009 ◽  
Author(s):  
P.M. Shankar ◽  
V.A. Dumane ◽  
C.W. Piccoli ◽  
J.M. Reid ◽  
F. Forsberg ◽  
...  
Keyword(s):  
Breast Masses ◽  
Computer Aided
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