Automated classification of cancer tissues using multispectral imagery

Automated classification of medical images for colorectal and prostate cancer diagnosis is a crucial tool for improving routine diagnosis decisions. Therefore, in the last few decades, there has been an increasing interest in refining and adapting machine learning algorithms to classify microscopic images of tumour biopsies. Recently, multispectral imagery has received a significant interest from the research community due to the fast-growing development of high-performance computers. This thesis investigates novel algorithms for automatic classification of colorectal and prostate cancer using multispectral imagery in order to propose a system outperforming the state-of-the-art techniques in the field. To achieve this objective, several feature extraction methods based on image texture have been investigated, analysed and evaluated. A novel texture feature for multispectral images is also constructed as an adaptation of the local binary pattern texture feature to multispectral images by expanding the pixels neighbourhood to the spectral dimension. It has the advantage of capturing the multispectral information with a limited feature vector size. This feature has demonstrated improved classification results when compared against traditional texture features. In order to further enhance the systems performance, advanced classification schemes such as bag-of-features - to better capture local information - and stacked generalisation - to select the most discriminative texture features - are explored and evaluated. Finally, the recent years have seen an accelerated and exponential rise of deep learning, boosted by the advances in hardware, and more specifically graphics processing units. Such models have demonstrated excellent results for supervised learning in multiple applications. This observation has motivated the employment in this thesis of deep neural network architectures, namely convolutional neural networks. Experiments were also carried out to evaluate and compare the performance obtained with the features extracted using convolutional neural networks with random initialisation against features extracted with pre-trained models on ImageNet dataset. The analysis of the classication accuracy achieved with deep learning models reveals that the latter outperforms the previously proposed texture extraction methods. In this thesis, the algorithms are assessed using two separate multiclass datasets: the first one consists of prostate tumour multispectral images, and the second contains multispectral images of colorectal tumours. The colorectal dataset was acquired on a wide domain of the light spectrum ranging from the visible to the infrared wavelengths. This dataset was used to demonstrate the improved results produced using infrared light as well as visible light

Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization

This paper proposes a new multispectral multiscale local binary pattern feature extraction method for automatic classification of colorectal and prostatic tumor biopsies samples. A multilevel stacked generalization classification technique is also proposed and the key idea of the paper considers a grade diagnostic problem rather than a simple malignant versus tumorous tissue problem using the concept of multispectral imagery in both the visible and near infrared spectra. To validate the proposed algorithm performances, a comparative study against related works using multispectral imagery is conducted including an evaluation on three different multiclass datasets of multispectral histology images: two representing images of colorectal biopsies - one dataset was acquired in the visible spectrum while the second captures near-infrared spectra. The proposed algorithm achieves an accuracy of 99.6% on the different datasets. The results obtained demonstrate the advantages of infrared wavelengths to capture more efficiently the most discriminative information. The results obtained show that our proposed algorithm outperforms other similar methods

Convolutional Neural Network–Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study

Background: Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra- and interobserver variability, which affects diagnosis reliability. Objective: This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis. Methods: In this study, we proposed a convolutional neural network (CNN) model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer. Results: Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for the whole classification task. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images. Conclusions: The proposed CNN architecture was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, and it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images

Multispectral imaging and machine learning for automated cancer diagnosis

Advancing technologies in the current era paved a lot to break the hurdles in medical diagnostic field. When cancer turned out to be the most common and dangerous disease of the age, novel diagnostic methodologies were introduced to enable early detection and hence save numerous lives. Accomplishment of various automatic and semi-automatic approaches in the diagnosis has proved its sufficient impetus to improve diagnostic speed and accuracy. A wide range of image processing based tools are currently available as a part of automatic cancer detection systems. Different imaging modalities have been utilized for extracting the suspected patient information, where the multispectral imaging has emerged as an efficient means for capturing the entire range of spectral and spatial data. In this paper, we review the current multispectral imaging based methods for automatic diagnosis of major types of cancer and discuss the limitations which are yet to be overcome, so as to improve the existing systems

Texture analysis for colorectal tumour biopsies using multispectral imagery

Colorectal cancer is one of the most common cancers in the world. As part of its diagnosis, a histological analysis is often run on biopsy samples. Multispecral imagery taken from cancer tissues can be useful to capture more meaningful features. However, the resulting data is usually very large having a large number of varying feature types. This papers aims to investigate and compare the performances of multispectral imagery taken from colorectal biopsies using different techniques for texture feature extraction inclduing local binary patterns, Haraclick features and local intensity order patterns. Various classifiers such as Support Vector Machine and Random Forest are also investigated. The results show the superiority of multispectral imaging over the classical panchromatic approach. In the multispectral imagery's analysis, the local binary patterns combined with Support Vector Machine classifier gives very good results achieving an accuracy of 91.3%

Urban greenhouse gases monitoring with the QualAir Fourier transform spectrometer in Paris

International audienceMonitoring greenhouse gases (GHGs) in large cities is becoming like air quality one of the priority environmental research areas for scientists and public health authorities. The QualAir platform at University Pierre et Marie Curie (UPMC), is an innovating experimental research platform dedicated to survey GHGs and urban air quality. As one of the major instruments of the QualAir platform, the ground-based Fourier transform spectrometer (QualAir FTS, IFS 125HR model) analyses the composition of the urban atmosphere of Paris, which is the third European megacity. The continous monitoring of GHGs and atmospheric pollutants are essential to improve the estimate of sources and sinks of GHGs and the understanding of urban air pollution processes. Associated with a sun-tracker, the QualAir remote sensing FTS operates in solar infrared absorption and enables to monitor many pollutants and GHGs, and to follow up their variability in the Ile-de-France region. A description of the QualAir FTS will be given. Concentrations of GHGs (CO2, CH4, N2O, …) are retrieved by the radiative transfer model PROFFIT. Located in the centre of Paris, the QualAir FTS can provide new and complementary urban measurements as compared to unpolluted ground-based stations of existing networks (NDACC and TCCON). We will show some first CO2 measurements acquired with our instrument in the framework of the French CO2-MEGAPARIS project, the main goal of which is to quantify CO2 emissions from Paris megacity. Such ground-based information will help to reduce uncertainties in carbon cycle models and to contribute to the characterization of regional GHGs fluxes, especially regarding anthropogenic emissions and trends