<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_1905-7415.1004.497_s_001_w2aab3b7b5b1b6b1aab1c16b1Aa"><h3>Background</h3><p>Detection of small breast lesions is a challenging task for radiologists. Computer aided detection (CAD) systems are implemented to aid radiologists in detecting masses and microcalcifications. This has the potential to raise the level of sensitivity in breast cancer detection.</p></sec><sec id="j_1905-7415.1004.497_s_002_w2aab3b7b5b1b6b1aab1c16b2Aa"><h3>Objectives</h3><p>To evaluate a new system to detect suggestions of suspicious small lesions.</p></sec><sec id="j_1905-7415.1004.497_s_003_w2aab3b7b5b1b6b1aab1c16b3Aa"><h3>Methods</h3><p>Small samples were extracted from different tissue types. Texture features were calculated, and the best features were selected using Waikato Environment for Knowledge Analysis (WEKA) software. Subsequently, 7 selected features were used to form a decision tree. To reduce false negative cases, fuzzy logic was used. In the implementation phase, input images were divided into 8 pixel ´ 8 pixel tiles. For each tile, all selected features were computed as fuzzy inputs.</p></sec><sec id="j_1905-7415.1004.497_s_004_w2aab3b7b5b1b6b1aab1c16b4Aa"><h3>Results</h3><p>To evaluate the technique, the suggested system was applied to 326 images obtained from the National Cancer Society of Malaysia. Based on this application, results showed that the suggested system has an acceptable sensitivity of 85.6% and specificity of 90.7%.</p></sec><sec id="j_1905-7415.1004.497_s_005_w2aab3b7b5b1b6b1aab1c16b5Aa"><h3>Conclusions</h3><p>The fuzzy system is a promising technique for early detection of breast cancer.</p></sec></abstract>

BackgroundDetection of small breast lesions is a challenging task for radiologists. Computer aided detection (CAD) systems are implemented to aid radiologists in detecting masses and microcalcifications. This has the potential to raise the level of sensitivity in breast cancer detection.ObjectivesTo evaluate a new system to detect suggestions of suspicious small lesions.MethodsSmall samples were extracted from different tissue types. Texture features were calculated, and the best features were selected using Waikato Environment for Knowledge Analysis (WEKA) software. Subsequently, 7 selected features were used to form a decision tree. To reduce false negative cases, fuzzy logic was used. In the implementation phase, input images were divided into 8 pixel ´ 8 pixel tiles. For each tile, all selected features were computed as fuzzy inputs.ResultsTo evaluate the technique, the suggested system was applied to 326 images obtained from the National Cancer Society of Malaysia. Based on this application, results showed that the suggested system has an acceptable sensitivity of 85.6% and specificity of 90.7%.ConclusionsThe fuzzy system is a promising technique for early detection of breast cancer.

Detection of masses and microcalcifications in digital mammogram images using fuzzy logic

Asian Biomedicine

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

BackgroundDetection of small breast lesions is a challenging task for radiologists. Computer aided detection (CAD) systems are implemented to aid radiologists...

Detection of masses and microcalcifications in digital mammogram images using fuzzy logic

Article Category: Brief communication (Original)

Online veröffentlicht: 31. Jan. 2017

Seitenbereich: 345 - 350

DOI: https://doi.org/10.5372/1905-7415.1004.497

Schlüsselwörter
Breast cancer, detection, fuzzy logic, mammogram, microcalcification

© 2016 Mostafa Langarizadeh, Rozi Mahmud, Rafat Bagherzadeh

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Detection of masses and microcalcifications in digital mammogram images using fuzzy logic

Article Category: Brief communication (Original)

Online veröffentlicht: 31. Jan. 2017

Seitenbereich: 345 - 350

DOI: https://doi.org/10.5372/1905-7415.1004.497

SchlüsselwörterBreast cancer, detection, fuzzy logic, mammogram, microcalcification

© 2016 Mostafa Langarizadeh, Rozi Mahmud, Rafat Bagherzadeh

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Breast cancer, detection, fuzzy logic, mammogram, microcalcification