Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization
The tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is desi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/3/6306 |
| _version_ | 1798042286710325248 |
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| author | Jong-Ha Lee Yoon Nyun Kim Hee-Jun Park |
| author_facet | Jong-Ha Lee Yoon Nyun Kim Hee-Jun Park |
| author_sort | Jong-Ha Lee |
| collection | DOAJ |
| description | The tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of tissue inclusion using finite element modeling (FEM). This forward information is used to develop an inversion algorithm that will be used to extract the size, depth, and Young's modulus of a tissue inclusion from the tactile data. We utilize the artificial neural network (ANN) for the inversion algorithm. The proposed estimation method was validated by a realistic tissue phantom with stiff inclusions. The experimental results showed that the proposed estimation method can measure the size, depth, and Young's modulus of a tissue inclusion with 0.58%, 3.82%, and 2.51% relative errors, respectively. The obtained results prove that the proposed method has potential to become a useful screening and diagnostic method for breast cancer. |
| first_indexed | 2024-04-11T22:33:22Z |
| format | Article |
| id | doaj.art-d49a3b38ec1c4e77b3419d881199e2b9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:33:22Z |
| publishDate | 2015-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d49a3b38ec1c4e77b3419d881199e2b92022-12-22T03:59:18ZengMDPI AGSensors1424-82202015-03-011536306632310.3390/s150306306s150306306Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue CharacterizationJong-Ha Lee0Yoon Nyun Kim1Hee-Jun Park2Department of Biomedical Engineering, School of Medicine, Keimyung University, 1095, Dalgubeol-daero, Daegu 704-701, KoreaDepartment of Internal Medicine, Dongsan Medical Center, Keimyung University, 1095, Dalgubeol-daero, Daegu 704-701, KoreaDepartment of Biomedical Engineering, School of Medicine, Keimyung University, 1095, Dalgubeol-daero, Daegu 704-701, KoreaThe tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of tissue inclusion using finite element modeling (FEM). This forward information is used to develop an inversion algorithm that will be used to extract the size, depth, and Young's modulus of a tissue inclusion from the tactile data. We utilize the artificial neural network (ANN) for the inversion algorithm. The proposed estimation method was validated by a realistic tissue phantom with stiff inclusions. The experimental results showed that the proposed estimation method can measure the size, depth, and Young's modulus of a tissue inclusion with 0.58%, 3.82%, and 2.51% relative errors, respectively. The obtained results prove that the proposed method has potential to become a useful screening and diagnostic method for breast cancer.http://www.mdpi.com/1424-8220/15/3/6306tumor detectionartificial palpationlesion characterizationtactile sensorbiomimetic sensorYoung’s modulus |
| spellingShingle | Jong-Ha Lee Yoon Nyun Kim Hee-Jun Park Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization Sensors tumor detection artificial palpation lesion characterization tactile sensor biomimetic sensor Young’s modulus |
| title | Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization |
| title_full | Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization |
| title_fullStr | Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization |
| title_full_unstemmed | Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization |
| title_short | Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization |
| title_sort | bio optics based sensation imaging for breast tumor detection using tissue characterization |
| topic | tumor detection artificial palpation lesion characterization tactile sensor biomimetic sensor Young’s modulus |
| url | http://www.mdpi.com/1424-8220/15/3/6306 |
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