Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)).  As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier

Biometric identification with 3D fingerprints acquired through optical coherence tomography

Orientador : Prof. Dr. Luciano SilvaCoorientador : Profª. Olga Regina Pereira BellonTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 28/06/2016Inclui referências : f. 75-82Área de concentraçãoResumo: Um método para se obter impressões digitais 3D da derme e da epiderme a partir de imagens em alta resolução adquiridas utilizando Tomografia de Coerência Ótica (OCT) é proposto neste trabalho. Este método, resolve limitações das técnicas de reconstrução 3D de impressões digitais que empregam múltiplas câmeras/triangulação ou iluminação estruturada, tais como variações de resolução do centro para as bordas das impressões digitais 3D causadas por erros de reconstrução, sensibilidade a baixa iluminação e contraste insuficiente. Uma técnica de busca e identificação baseados em padrões inovativos, os "mapas KH " (usados para a segmentação de regiões de superfície em imagens de intensidade e de profundidade), extraídos computando as curvaturas Gaussiana (K) e média (H) de uma região de interesse na vizinhança das minúcias (denominada nuvem de minúcia), é apresentada. Grandes bases de mapas KH, uma para cada nuvem de minúcia identificada, podem ser construídos com essa técnica. A estratégia de busca e identificação, em duas etapas, baseia-se primeiro em padrões locais de gradientes (LGP) dos mapas KH, para reduzir o espaço de busca dentro da base, seguidos de uma comparação que utiliza uma medida de similaridade, a correlação cruzada normalizada dos padrões pré-selecionados com o LGP com os que se quer identificar. A acuracidade do método e sua compatibilidade com os métodos correntes, comparável ou superior à dos métodos 2D, é verificada através da identificação biométrica de impressões digitais 3D utilizando duas bases de imagens, uma adquirida através da tecnologia OCT e a outra gentilmente cedida pela Universidade Politécnica de Hong Kong. A base de imagens OCT, a primeira adquirida com essa tecnologia, é composta de imagens coletadas de onze voluntários em duas sessões de escaneamento e contém imagens de dedos de pessoas com diferentes idades, gênero e etnias e contém casos de cicatrizes, calos e alterações, tais como abrasão e arranhões. Uma base de impressões digitais 2D, obtida dos mesmos voluntários através de um leitor regular de impressões digitais, foi adquirida para permitir uma comparação da técnica proposta com os métodos de identificação tradicionais. A aplicabilidade do método proposto à identificação de impressões digitais alteradas, deterioradas acidentalmente ou intencionalmente, é investigada. Nesses casos, a impressão digital 3D extraída da derme e compatível com a da epiderme é empregada. A identificação destas impressões 3D alteradas é testada utilizando a base de imagens adquiridas com OCT. A acuracidade da técnica é comparada com a obtida utilizando os métodos tradicionais 2D usando os gráficos de taxas de Falsa Aceitação e Falsa Rejeição (FAXxFRR) e de Características Cumulativas de Identificação (CMC). Impressões digitais 2D, extraídas a partir das impressões digitais 3D simulando o rolamento do dedo durante a aquisição (rolamento virtual), foram geradas e sua compatibilidade com as bases de imagens 2D foi testada. Um conjunto de medidas de avaliação de qualidade foram aplicados às bases de imagens de impressões digitais 3D e sua correspondência aos escores de identificação foi analisada para determinar aqueles que podem contribuir para melhorar a acuracidade da identificação. Palavras-chave: Impressões digitais 3D. Identificação Biométrica. Tomografia de Coerência Ótica.Abstract: A method to obtain epidermal and dermal 3D fingerprints from high-resolution images acquired using Optical Coherence Tomography (OCT) is proposed. This method addresses limitations of current 3D reconstruction techniques that employ multiple cameras/triangulation or structured illumination such as depth and resolution variations from the center to the borders of the fingerprint caused by reconstruction errors, sensitivity to low illumination and poor contrast. The availability of these 3D fingerprints allowed the creation of new matching methods that benefit from the rich information available in 3D. A 3D fingerprint matching technique based on novel patterns, the KH maps (used to surface region segmentation in range and intensity images), extracted by computing the Gaussian and mean curvatures (SILVA; BELLON; GOTARDO, 2001) from a region of interest around the minutiae, named minutiae clouds is presented. Large databases of KH maps, one for each identified minutiae cloud can be built. The matching strategy, a two-step approach, relies on local gradient patterns (LGP) of the KH maps to narrow the search space, followed by a similarity matching, the normalized cross correlation of patterns being matched. The accuracy and matching compatibility, comparable or improved in relation to the 2D matching methods, is verified through matching 3D fingerprints from two databases one acquired using OCT and a public database gently made available by the Hong Kong Polytechnic University. The OCT database, the first 3D database acquired using Optical Coherence Tomography, to our knowledge, is made of images collected from eleven volunteers in two scanning sessions and contains images of people of different ages, genders and ethnicities and also cases of scars, calluses and alterations as abrasion and scratches. A 2D fingerprint database, scanned from the same volunteers using a regular fingerprint reader was also obtained for comparison with traditional matching methods. We investigate the applicability of our method to the identification of altered fingerprints, damaged unintentionally or accidentally. In these cases, the 3D dermal fingerprint, compatible with the epidermis fingerprint, is employed. Matching with 3D dermal and epidermal fingerprints is tested in the OCT database. Matching accuracy is compared with the obtained using traditional matching 2D methods by using False Acceptance and False rejection rate (FARxFRR) and Cumulative Matching Characteristics (CMC) graphs. Unwrapped fingerprints, 2D fingerprints extracted from 3D fingerprints by virtual unrolling were generated and tested for compatibility with 2D databases. A set of quality evaluation measures were employed to the 3D fingerprint databases and their correspondence to the matching scores was analyzed to identify those that can contribute to improve the matching accuracy. Key-words: 3D Fingerprints. Biometric identification. Optical Coherence Tomography

Combining local descriptors and classification methods for human emotion recognition.

Masters Degree. University of KwaZulu-Natal, Durban.Human Emotion Recognition occupies a very important place in artificial intelligence and has several applications, such as emotionally intelligent robots, driver fatigue monitoring, mood prediction, and many others. Facial Expression Recognition (FER) systems can recognize human emotions by extracting face image features and classifying them as one of several prototypic emotions. Local descriptors are good at encoding micro-patterns and capturing their distribution in a sub-region of an image. Moreover, dividing the face into sub-regions introduces information about micro-pattern locations, essential for developing robust facial expression features. Hence, local descriptors’ efficiencies depend heavily on parameters such as the sub-region size and histogram length. However, the extraction parameters are seldom optimized in existing approaches. This dissertation reviews several local descriptors and classifiers, and experiments are conducted to improve the robustness and accuracy of existing FER methods. A study of the Histogram of Oriented Gradients (HOG) descriptor inspires this research to propose a new face registration algorithm. The approach uses contrast-limited histogram equalization to enhance the image, followed by binary thresholding and blob detection operations to rotate the face upright. Additionally, this research proposes a new method for optimized FER. The main idea behind the approach is to optimize the calculation of feature vectors by varying the extraction parameter values, producing several feature sets. The best extraction parameter values are selected by evaluating the classification performances of each feature set. The proposed approach is also implemented using different combinations of local descriptors and classification methods under the same experimental conditions. The results reveal that the proposed methods produced a better performance than what was reported in previous studies. Furthermore, the results showed an improvement of up to 2% compared with the performance achieved in previous works. The results showed that HOG was the most effective local descriptor, while Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) were the best classifiers. Hence, the best combinations were HOG+SVM and HOG+MLP

Local gradient increasing pattern for facial expression recognition

This paper presents a new facial descriptor for facial expression recognition based on the Local Gradient Increasing Pattern (LGIP). A LGIP feature is to encode the intensity increasing trends in eight directions at each pixel using eight binary bits, and then a decimal code is assigned to describe the over-all increasing trend. The facial descriptor is generated from grid-based regional LGIP histograms. Subsequently, Support Vector Machine classifier is used for multi-class expression classification. Extensive experiments using Cohn-Kanade and Jaffe databases show that the LGIP based descriptor outperforms other related algorithms