Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

Autor: Pedro M. Vieira, Nuno R. Freitas, Carlos S. Lima, Estevão Lima
Přispěvatelé: Universidade do Minho
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Discrete wavelet transform
Support Vector Machine
Computer science
0206 medical engineering
Urinary Bladder
030232 urology & nephrology
Wavelet Analysis
Image processing
02 engineering and technology
HSL and HSV
Color space
Pattern Recognition
Automated
03 medical and health sciences
0302 clinical medicine
Wavelet
Segmentation
Bladder tumor
medicine
Image Processing
Computer-Assisted
Humans
Multilayer perceptron
Radiology
Nuclear Medicine and imaging
Diagnosis
Computer-Assisted
Aged
Aged
80 and over
Bladder cancer
Science & Technology
Radiological and Ultrasound Technology
business.industry
Pattern recognition
Cystoscopy
Middle Aged
medicine.disease
020601 biomedical engineering
3. Good health
Support vector machine
Urinary Bladder Neoplasms
Computer-aided diagnosis
Case-Control Studies
Artificial intelligence
business
Algorithms
Zdroj: Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Popis: Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.
This work is supported by FCT under Project No. UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI) under Project No. POCI-01-0145-FEDER-006941.
info:eu-repo/semantics/publishedVersion
Databáze: OpenAIRE
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