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

Targeted Multispectral Filter Array Design for Endoscopic Cancer Detection in the Gastrointestinal Tract

Colour differences between healthy and diseased tissue in the gastrointestinal tract are detected visually by clinicians during white light endoscopy (WLE); however, the earliest signs of disease are often just a slightly different shade of pink compared to healthy tissue. Here, we propose to target alternative colours for imaging to improve contrast using custom multispectral filter arrays (MSFAs) that could be deployed in an endoscopic chip-on-tip configuration. Using an open-source toolbox, Opti-MSFA, we examined the optimal design of MSFAs for early cancer detection in the gastrointestinal tract. The toolbox was first extended to use additional classification models (k-Nearest Neighbour, Support Vector Machine, and Spectral Angle Mapper). Using input spectral data from published clinical trials examining the oesophagus and colon, we optimised the design of MSFAs with 3 to 9 different bands. We examined the variation of the spectral and spatial classification accuracy as a function of number of bands. The MSFA designs have high classification accuracies, suggesting that future implementation in endoscopy hardware could potentially enable improved early detection of disease in the gastrointestinal tract during routine screening and surveillance. Optimal MSFA configurations can achieve similar classification accuracies as the full spectral data in an implementation that could be realised in far simpler hardware. The reduced number of spectral bands could enable future deployment of multispectral imaging in an endoscopic chip-on-tip configuration.Comment: 29 page

Integration of Spatial and Spectral Information for Hyperspectral Image Classification

Hyperspectral imaging has become a powerful tool in biomedical and agriculture fields in the recent years and the interest amongst researchers has increased immensely. Hyperspectral imaging combines conventional imaging and spectroscopy to acquire both spatial and spectral information from an object. Consequently, a hyperspectral image data contains not only spectral information of objects, but also the spatial arrangement of objects. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. Therefore, this dissertation investigates the integration of information from both the spectral and spatial domains to enhance hyperspectral image classification performance. The major impediment to the combined spatial and spectral approach is that most spatial methods were only developed for single image band. Based on the traditional singleimage based local Geary measure, this dissertation successfully proposes a Multidimensional Local Spatial Autocorrelation (MLSA) for hyperspectral image data. Based on the proposed spatial measure, this research work develops a collaborative band selection strategy that combines both the spectral separability measure (divergence) and spatial homogeneity measure (MLSA) for hyperspectral band selection task. In order to calculate the divergence more efficiently, a set of recursive equations for the calculation of divergence with an additional band is derived to overcome the computational restrictions. Moreover, this dissertation proposes a collaborative classification method which integrates the spectral distance and spatial autocorrelation during the decision-making process. Therefore, this method fully utilizes the spatial-spectral relationships inherent in the data, and thus improves the classification performance. In addition, the usefulness of the proposed band selection and classification method is evaluated with four case studies. The case studies include detection and identification of tumor on poultry carcasses, fecal on apple surface, cancer on mouse skin and crop in agricultural filed using hyperspectral imagery. Through the case studies, the performances of the proposed methods are assessed. It clearly shows the necessity and efficiency of integrating spatial information for hyperspectral image processing

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization

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

Two-Level Data Augmentation with Transfer Learning for Classification of Medical Images with Limited Data

Machine learning used in the medical industry can potentially detect cancer in humancells at an early stage. However, training the machine learning models, especially deep learning models require thousands to millions of samples in order to reach an acceptable accuracy level. It is well-know that obtaining medical data is tedious hence in most cases, medical datasets have limited number of data samples. One solution for this problem is utilizing transfer learning such as pretrained networks on another dataset. Another solution is to increase the number of training data points with data augmentation. Common data augmentation methods for images include not only simple techniques such as transforming images using rotation and flipping, but also generative adversarial networks (GANs). However, one critical question is “Does the original dataset have enough to train a GAN?”. In most scenarios, the answer is “No” for this critical question. In this thesis, we propose a two-level data augmentation technique (simple data augmentation based on image transformations followed by a GAN) with transfer learning, which is tested on a small dataset of cancer cell images. The dataset used in this research consists of lung and colon cancer samples, each containing different types of cancers. Only part of the original dataset is used for experimenting in order to mimic small dataset environment. Our results show that the proposed method is able to achieve an accuracy of 94.1% even when 150 original images used for training. This is very close to 97.33% accuracy achieved if one uses all the available training data which is 12000 samples

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications