Abstract
The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of a large number of spectral bands within the visible spectrum. This results in a feature vector of size greater than 100. For such a high dimensionality, pattern recognition techniques suffer from the well-known curse of dimensionality problem. The two well-known techniques to solve this problem are feature extraction and feature selection. In this paper, a novel feature selection technique using tabu search with an intermediate-term memory is proposed. The cost of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier. The experiments have been carried out on the prostate cancer textured multispectral images and the results have been compared with a reported classical feature extraction technique. The results have indicated a significant boost in the performance both in terms of minimizing features and maximizing classification accuracy.
Highlights
Prostate cancer has become the second most commonly diagnosed cancer in the male population after lung cancer, with approximately 22 800 new cases diagnosed every year in the UK alone
We present a tabu search algorithm, where the quality of a solution is characterized by a fuzzy logic rule expressed in linguistic variables of the problem domain
A tabu search method with intermediate-term memory is proposed for feature selection problem of large feature vector size
Summary
Prostate cancer has become the second most commonly diagnosed cancer in the male population after lung cancer, with approximately 22 800 new cases diagnosed every year in the UK alone. Prostate needle biopsy remains the only conclusive way to make an accurate diagnosis of prostate cancer [1]. Roula et al have described a novel approach in which additional spectral data is used for the classification of prostate needle biopsies [2, 3]. The aim of this novel approach is to help pathologists reduce the diagnosis error rate. Instead of analyzing conventional grey scale or RGB color images, spectral bands have been used in the analysis. Results have shown that the multispectral image classification outperforms both RGB and grey-levelbased classification.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
