Spectral analysis of breast ultrasound data with application to mass sizing and characterization
<p>Ultrasound is a commonly used imaging modality in diagnosis and pre-operative assessment of breast masses. However, radiologists often find it very difficult to correctly size masses using conventional ultrasound images. Consequently, there exists a strong need for more accurate sizing tool...
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| Format: | Thesis |
| Language: | English |
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2014
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| _version_ | 1797109968735830016 |
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| author | Teixeira Ribeiro, R |
| author2 | Noble, A |
| author_facet | Noble, A Teixeira Ribeiro, R |
| author_sort | Teixeira Ribeiro, R |
| collection | OXFORD |
| description | <p>Ultrasound is a commonly used imaging modality in diagnosis and pre-operative assessment of breast masses. However, radiologists often find it very difficult to correctly size masses using conventional ultrasound images. Consequently, there exists a strong need for more accurate sizing tools to avoid either the removal of an over-estimated amount of tissue or a second surgical procedure to remove margins involved by tumour not removed in the primary operation.</p> <p>In this thesis, we propose a new method of processing the backscattered ultrasound signals from breast tissue (based on the Fourier spectral analysis) to better estimate the degree of echogenicity and generate parametric images where the visibility of breast mass boundaries is improved (SPV parametric image). Moreover, an algorithm is proposed to recover some anatomical structures (particularly, Cooper’s ligaments) which are shadowed during the image acquisition process (LWSPV parametric image). The information from both algorithms is combined to generate a final SPV+LWSPV parametric image.</p> <p>A 20-case pilot study was conducted on clinical data, which showed that the SPV+LWSPV parametric image added useful information to the B-mode image for clinical assessment in 85% of the cases (increase in diagnostic confidence in at least one boundary). Moreover, in 35% of the cases, the SPV+LWSPV parametric image provided a better definition of the entire boundary. Note that the radiologist knew the final diagnosis from histopathology.</p> <p>In addition, the SPV+LWSPV method has the advantage that it uses the I/Q data from a standard ultrasound equipment without the need for additional hardware.</p> <p>On the basis of these facts, we believe there to be a case for further investigation of the SPV+LWSPV imaging as a useful clinical tool in the pre-operative assessment of breast mass boundaries.</p> |
| first_indexed | 2024-03-07T07:48:38Z |
| format | Thesis |
| id | oxford-uuid:8768959f-cc5a-476d-b924-5a5d7df31b8d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:48:38Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:8768959f-cc5a-476d-b924-5a5d7df31b8d2023-06-27T08:35:28ZSpectral analysis of breast ultrasound data with application to mass sizing and characterizationThesishttp://purl.org/coar/resource_type/c_db06uuid:8768959f-cc5a-476d-b924-5a5d7df31b8dBiomedical engineeringImage understandingMedical EngineeringEnglishOxford University Research Archive - Valet2014Teixeira Ribeiro, RNoble, A<p>Ultrasound is a commonly used imaging modality in diagnosis and pre-operative assessment of breast masses. However, radiologists often find it very difficult to correctly size masses using conventional ultrasound images. Consequently, there exists a strong need for more accurate sizing tools to avoid either the removal of an over-estimated amount of tissue or a second surgical procedure to remove margins involved by tumour not removed in the primary operation.</p> <p>In this thesis, we propose a new method of processing the backscattered ultrasound signals from breast tissue (based on the Fourier spectral analysis) to better estimate the degree of echogenicity and generate parametric images where the visibility of breast mass boundaries is improved (SPV parametric image). Moreover, an algorithm is proposed to recover some anatomical structures (particularly, Cooper’s ligaments) which are shadowed during the image acquisition process (LWSPV parametric image). The information from both algorithms is combined to generate a final SPV+LWSPV parametric image.</p> <p>A 20-case pilot study was conducted on clinical data, which showed that the SPV+LWSPV parametric image added useful information to the B-mode image for clinical assessment in 85% of the cases (increase in diagnostic confidence in at least one boundary). Moreover, in 35% of the cases, the SPV+LWSPV parametric image provided a better definition of the entire boundary. Note that the radiologist knew the final diagnosis from histopathology.</p> <p>In addition, the SPV+LWSPV method has the advantage that it uses the I/Q data from a standard ultrasound equipment without the need for additional hardware.</p> <p>On the basis of these facts, we believe there to be a case for further investigation of the SPV+LWSPV imaging as a useful clinical tool in the pre-operative assessment of breast mass boundaries.</p> |
| spellingShingle | Biomedical engineering Image understanding Medical Engineering Teixeira Ribeiro, R Spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| title | Spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| title_full | Spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| title_fullStr | Spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| title_full_unstemmed | Spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| title_short | Spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| title_sort | spectral analysis of breast ultrasound data with application to mass sizing and characterization |
| topic | Biomedical engineering Image understanding Medical Engineering |
| work_keys_str_mv | AT teixeiraribeiror spectralanalysisofbreastultrasounddatawithapplicationtomasssizingandcharacterization |