Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to classify the segments and the whole epithelium. The experimental data consists of 65 images. The proposed method accuracy is 77.25% compared to 75.75% using the previous method for the same dataset

Epithelium detection and cervical intraepithelial neoplasia classification in digitized histology images

“Cervical cancer is one of the most deadly cancers faced by women. It is the second leading cause of cancer death in women aged 20 to 39 years. In order to detect cancer at early stages, pathologists analyze the epithelium region from the cervical histology images. These histology images have a pre-cervical cancer condition called cervical intraepithelial neoplasia (CIN) determined by pathologists. This study deals with automating the process of epithelium detection and epithelium CIN classification in digitized histology images. For epithelium detection, the objective is to detect epithelium regions in microscopy images from non-epithelium regions and background. convolutional neural networks, both shallow and deep networks are used for epithelium detection. The highest epithelium detection accuracy of 98.84% is obtained using transfer learning on VGG-19 architecture, pre-trained on the ImageNet dataset. For CIN classification, the epithelium region is divided into 5 segments along the medial axis and patches from each segment were used for training the deep learning model. Vertical segment level classification probabilities from deep learning model are obtained and further classified using SVM, LDA, MLP, logistic and RF classifiers. The highest image level accuracy obtained is 77.27% for MLP classifier using voting”--Abstract, page iii

Deep learning and localized features fusion for medical image classification

Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible. This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations. For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method --Abstract, page iv

Nuclei segmentation of histology images based on deep learning and color quantization and analysis of real world pill images

Medical image analysis has paved a way for research in the field of medical and biological image analysis through the applications of image processing. This study has special emphasis on nuclei segmentation from digitized histology images and pill segmentation. Cervical cancer is one of the most common malignant cancers affecting women. This can be cured if detected early. Histology image feature analysis is required to classify the squamous epithelium into Normal, CIN1, CIN2 and CIN3 grades of cervical intraepithelial neoplasia (CIN). The nuclei in the epithelium region provide the majority of information regarding the severity of the cancer. Segmentation of nuclei is therefore crucial. This paper provides two methods for nuclei segmentation. The first approach is clustering approach by quantization of the color content in the histology images uses k-means++ clustering. The second approach is deep-learning based nuclei segmentation method works by gathering localized information through the generation of superpixels and training convolutional neural network. The other part of the study covers segmentation of consumer-quality pill images. Misidentified and unidentified pills constitute a safety hazard for both patients and health professionals. An automatic pill identification technique is essential to address this challenge. This paper concentrates on segmenting the pill image, which is crucial step to identify a pill. A color image segmentation algorithm is proposed by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adjacency graphs. The algorithm manages to supersede the challenges due to various backgrounds and lighting conditions of consumer-quality pill images --Abstract, page iii

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv

Cervical cancer histology image feature extraction and classification

Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development from vertical segment partitions of the epithelium region for the same digitized histology images. Using features developed in this thesis with prior work, a particle swarm optimization and Receiver Operating Characteristic curve (ROC) explored for CIN classification showing exact grade labeling accuracy as high as 90%. --Abstract, page iii

Uterine cervical cancer histology image feature extraction and classification

The current study presents the investigation and development of image processing, computational intelligence, fuzzy logic, and statistical techniques for different types of data fusion for a varied range of applications. Raw data, decision level and feature level fusion techniques are explored for detection of pre Cervical cancer (CIN) grades from digital histology images of the cervical epithelium tissues. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis. The approach included medial axis determination, vertical segment partitioning as medial axis orthogonal cuts, individual vertical segment feature extraction and classification, and image-based classification using a voting scheme to fuse the vertical segment CIN grades. This paper presents advances in medial axis determination, epithelium atypical cell concentration feature development and a particle swarm optimization neural network and receiver operating characteristic curve technique for individual vertical segment-based classification. Combining individual vertical segment classification confidence values using a weighted sum fusion approach for image-based classification, exact grade labeling accuracy was as high as 90% for a 62-image data set --Abstract, page iii

Deep learning for digitized histology image analysis

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In this research, different facets of an automated digitized histology epithelium assessment pipeline have been explored to mimic the pathologist diagnostic approach. The entire pipeline from slide to epithelium CIN grade has been designed and developed using deep learning models and imaging techniques to analyze the whole slide image (WSI). The process is as follows: 1) identification of epithelium by filtering the regions extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based nuclei detection by superpixels was performed as an extension of our research. Results from this research indicate an improved performance of CIN assessment over state-of-the-art methods for nuclei segmentation, epithelium segmentation, and CIN classification, as well as the development of a prototype WSI-level tool”--Abstract, page iv

Uma proposta de arquitetura de alto desempenho para sistemas PACS baseada em extensões de banco de dados

Orientador : Prof. Dr. Aldo Von WangenheimTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 25/07/2014Inclui referênciasResumo: O uso de imagens digitais no processo de diagnóstico médico é observável em diferentes escalas e cenários de aplicação, tendo evoluído em termos de volume de dados adquiridos e número de modalidades de exame atendidas. A organização desse conteúdo digital, comumente representado por conjuntos de imagens no pa-drão DICOM (Digital Imaging and Communications in Medicine), costuma ser dele-gada a sistemas PACS (Picture Archiving and Communication System) baseados na agregação de componentes heterogêneos de hardware e software. Parte desses componentes interage de forma a compor a camada de armazenamento do PACS, responsável pela persistência de toda e qualquer imagem digital que, em algum momento, foi adquirida ou visualizada/manipulada via sistema. Apesar de emprega-rem recursos altamente especializados como SGBDs (Sistemas Gerenciadores de Banco de Dados), as camadas de armazenamento PACS atuais são visualizadas e utilizadas como simples repositórios de dados, assumindo um comportamento pas-sivo (ou seja, sem a agregação de regras de negócio) quando comparadas a outros componentes do sistema. Neste trabalho, propõe-se uma nova arquitetura PACS simplificada baseada em alterações na sua camada de armazenamento. As alterações previstas baseiam-se na troca do perfil passivo assumido atualmente por essa camada por um perfil ativo, utilizando-se de recursos de extensibilidade e de distribuição de dados (hoje não empregados) disponibilizados por seus componentes. A arquitetura proposta concentra-se na comunicação e no armazenamento de dados, utilizando-se de ex-tensões de SGBDs e de estruturas heterogêneas para armazenamento de dados convencionais e não convencionais, provendo alto desempenho em termos de es-calabilidade, suporte a grandes volumes de conteúdo e processamento descentrali-zado de consultas. Estruturalmente, a arquitetura proposta é formada por um con-junto de módulos projetados de forma a explorar as opções de extensibilidade pre-sentes em SGBDs, incorporando características e funcionalidades originalmente dis-tribuídas entre outros componentes do PACS (na forma de regras de negócio). Em nível de protótipo, resultados obtidos a partir de experimentos indicam a viabilidade de uso da arquitetura proposta, explicitando ganhos de desempenho na pesquisa de metadados e na recuperação de imagens DICOM quando comparados a arquiteturas PACS convencionais. A flexibilidade da proposta quanto à adoção de tecnologias de armazenamento heterogêneas também é avaliada positivamente, permitindo estender a camada de armazenamento PACS em termos de escalabili-dade, poder de processamento, tolerância a falhas e representação de conteúdo. Palavras-chave: PACS, DICOM, SGBD, extensibilidade, alto desempenho.Abstract: The use of digital images on medical diagnosis is observable in a number of application scenarios and in different scales, growing in terms of volume of data and contemplated medical specialties. To organize this digital content composed by image datasets in DICOM (Digital Imaging and Communications in Medicine), it is usual to adopt PACS (Picture Archiving and Communication System), an architecture built as an aggregation of hardware and software components. Some of these components compose the so-called PACS's storage layer, responsible for the persistence of every digital image acquired or visualized/manipulated through the system. Despite their high-specialized components (e.g., DBMS - Database Management System), PACS storage layers used today are visualized as simple data repositories, assuming a passive role (i.e., without the implementation of business rules) when compared to other components. In this work, a simplified, new architecture is proposed for PACS, based in modifications on its storage layer. The modifications are based in the replacement of the current passive role by an active one, using extensibility and data distribution resources available on its components. The proposed architecture focuses on communication and data storage, using DBMS extensions and heterogeneous structures for the storage of conventional and non-conventional data, providing high-performance in terms of scalability, support to large volumes of data and decentralized query processing. Structurally, the proposed architecture is composed by a set of modules designed to explore extensibility options available in DBMSs, incorporating characteristics and functionalities originally distributed as business rules among other components of PACS. At prototype level, results obtained through experiments indicate the viability of the proposal, making explicit the performance gains in the search for metadata and image retrieval when compared to conventional PACS architectures. The flexibility of the proposal regarding the adoption of heterogeneous storage technologies is also positively evaluated, allowing the extension of the PACS storage layer in terms of scalability, processing power, fault tolerance and content representation. Keywords: PACS, DICOM, DBMS, extensibility, High-Performance Computing