Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

SIGNAL PROCESSING FOR RAMAN SPECTRA FOR DISEASE DETECTION

Raman Spectroscopy enables in-depth study into the molecular structure of solid, liquid and gasses from its scattering spectrum. As such, the spectrum could offer a biochemical fingerprint to identify unknown molecules. Surface Enhanced Raman Spectroscopy (SERS) amplifies the weak Raman signal by 10+3 to 10+7 times, revolutionary making the method appealing to the research community. SERS has been proven useful for disease detection from a medium such as a cell, serum, urine, plasma, saliva, tears. The spectra displayed are noisy and complicated by the presence of other molecules, besides the targeted one. Moreover, the difference between the infected and controlled samples is far too minute for detection by the naked human eyes. Hence, signal processing techniques are found crucial to single out fingerprint of the target molecule from biological spectra. Our work here examines signal processing techniques attempted on SERS spectra for disease detection, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression Analysis (LRA). It is found that PCA-LDA is the most popular (45%), ensued by PCA-ANN (33%) and SVM (22%). PCA-SVM yields the highest in accuracy (99.9%), followed by PCA-ANN (98%) and LRA (97%). PCA-LDA and SVM score the highest in both sensitivity-specificity.Keywords: Raman Spectra, Surface Enhanced Raman Spectroscopy (SERS), Neural Network (NN), Support Vector Machine (SVM), Logistic Regression Analysis (LRA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)

Support vector machine for optical diagnosis of cancer

We report the application of a support vector machine (SVM) for the development of diagnostic algorithms for optical diagnosis of cancer. Both linear and nonlinear SVMs have been investigated for this purpose. We develop a methodology that makes use of SVM for both feature extraction and classification jointly by integrating the newly developed recursive feature elimination (RFE) in the framework of SVM. This leads to significantly improved classification results compared to those obtained when an independent feature extractor such as principal component analysis (PCA) is used. The integrated SVM-RFE approach is also found to outperform the classification results yielded by traditional Fisher's linear discriminant (FLD)-based algorithms. All the algorithms are developed using spectral data acquired in a clinical in vivo laser-induced fluorescence (LIF) spectroscopic study conducted on patients being screened for cancer of the oral cavity and normal volunteers. The best sensitivity and specificity values provided by the nonlinear SVM-RFE algorithm over the data sets investigated are 95 and 96% toward cancer for the training set data based on leave-one-out cross validation and 93 and 97% toward cancer for the independent validation set data. When tested on the spectral data of the uninvolved oral cavity sites from the patients it yielded a specificity of 85%

Improving Clinical Diagnosis of Melanocytic Skin Lesions by Raman Spectroscopy

High-quality Raman signals from melanocytic lesions compatible with a possible clinical application have not been demonstrated yet. The objectives of the work described in this thesis were: I: The development of a Raman spectroscopic prototype for objective and fast assessment of melanocytic skin lesions clinically suspicious for melanoma; II: Identification of the main spectroscopic features of melanoma and benign melanocytic lesions suspicious for melanoma; III: Assessment of the feasibility of Raman spectroscopy as an adjunct technique to improve clinical diagnosis of melanocytic skin lesions

Near-infrared raman spectroscopy for early detection of cervical precancer

Ph.DDOCTOR OF PHILOSOPH

Optimisation of machine learning methods for cancer detection using vibrational spectroscopy

Early cancer detection drastically improves the chances of cure and therefore methods are required, which allow early detection and screening in a fast, reliable and inexpensive manner. A prospective method, featuring all these characteristics, is vibrational spectroscopy. In order to take the next step towards the development of this technology into a clinical diagnostic tool, classification and imaging methods for an automated diagnosis based on spectral data are required. For this study, Raman spectra, derived from axillary lymph node tissue from breast cancer patients, were used to develop a diagnostic model. For this purpose different classification methods were investigated. A support vector machine (SVM) proved to be the best choice of classification method since it classified 100% of the unseen test set correctly. The resulting diagnostic models were thoroughly tested for their robustness to the spectral corruptions that would be expected to occur during routine clinical analysis. It showed that sufficient robustness is provided for a future diagnostic routine application. SVMs demonstrated to be a powerful classifier for Raman data and due to that they were also investigated for infrared spectroscopic data. Since it was found that a single SVM was not capable of reliably predicting breast cancer pathology based on tissue calcifications measured by infrared micro-spectroscopy a SVM ensemble system was implemented. The resulting multi-class SVM ensemble predicted the pathology of the unseen test set with an accuracy of 88.9%, in comparison a single SVM assessed with the same unseen test set achieved 66.7% accuracy. In addition, the ensemble system was extended for analysing complete infrared maps obtained from breast tissue specimens. The resulting imaging method successfully detected and staged calcification in infrared maps. Furthermore, this imaging approach revealed new insights into the calcification process in malignant development, which was not previously well understood.EThOS - Electronic Theses Online ServiceGBUnited Kingdo