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

Speed up Robust Features (SURF) with Principal Component Analysis-Support Vector Machine (PCA-SVM) for benign and malignant classifications

A novel Computer Aided Diagnosis (CADx) component is proposed for breast cancer classifications. Four major phases were conducted in this research. The first phase is pre-processing, this is followed by features extraction phase by using the Speed Up Robust Features (SURF). The next phase is features selection by using the Principal Component Analysis (PCA). The final phase is the classification phase to classify the cancer. Three different classifiers; Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Decision Tree (DT) were compared in this research. Results obtained shows that the PCA-SVM performs the highest accuracy with 92.9% accurate compared to other classifiers.Keywords: breast cancer; CADx; SURF; PCA; SV

Analysis of Mammographic Images for Early Detection of Breast Cancer Using Machine Learning Techniques

Breast cancer is the main reason for death among women. Radiographic images obtained from mammography equipment are one of the most frequently used techniques for helping in early detection of breast cancer. The motivation behind this study is to focus the tumour types of breast cancer images .It is methodology to anticipated a sickness in view of the visual conclusion of breast disease tumour types with precision, particularly when numerous feature are related. Breast Cancer (BC) is one such sample where the phenomenon is very complex furthermore numerous feature of tumour types are included. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using mammograms image processing techniques .The pattern recognition techniques for tumour image enhancements, segmentation, texture based image feature extraction and subsequent classification of breast cancer mammogram image was successfully performed. When two machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) were used to classify 120 images, it was observed from the results that Artificial Neural Network classifiers demonstrated the h classification rate 91.31% and the SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 92.11% and RBF classification rate is 92.85%

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging

Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples