Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

In this paper, a unified algorithm for extracting gray level profiles of Human chromosomes is presented. It is a unified approach since we do not discriminate chromosomes as straight and bended.  This is a very helpful procedure which extends the domain of success of most of the previously reported algorithms to highly curved chromosomes. The gray image of the chromosome is thresholded at the gray level 0.9, and the matrix of gray image is transformed into a list of pixel coordinates whose gray level is less than 0.9. To the list of two dimensional points, the most appropriate smooth curve is fitted. Then this smooth curve subdivided into n arcs of equal lengths, and straight lines are drawn that are normal to the curve at the end points of the subdivision. The points of the list are classified into n bins according to their distance to these n straight lines. The average of gray levels of each bin gives the gray levels at the points of the gray level profile of the chromosome. It is seen that the gray level profiles of the bended chromosomes have a high similarity with the straight counterparts

Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%

ENERGY MINIMIZATION FOR IDENTIFICATION OF BANDING PATTERN IN CHROMOSOMES USING OPTIMIZED GRAPH CUT ALGORITHM

Intensity inhomogeneity is a significant cause in reducing the accuracy of image segmentation. This paper proposes an algorithm for identification of bands in chromosomes using graph cut segmentation that uses global and local image statistics. The global energy is an estimate of the intensity distribution of the image and background and local energy provide the information related with neighboring pixels that eliminates the impact of intensity inhomogeneities. Efficient energy minimization helps in better pixel labeling and this is done by optimized Graph cut process. The shape prior of the band at each location of the image is considered with shape probability energy functions. The experimental results demonstrate that the approach is robust and efficient in detecting the band information in chromosomes to a larger extent

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

UAV-Multispectral Sensed Data Band Co-Registration Framework

Precision farming has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farms to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among the bands, which makes the alignment process complex. In this work, we propose a new framework for the band co-registration process based on two premises: i) the natural misalignment is an attribute of the camera, so it does not change during the acquisition process; ii) the speed of displacement of the UAV when compared to the speed between the acquisition of the first to the last band, is not sufficient to create significant distortions. We compared our results with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of 0.425 pixels, this result being 335% better than the evaluated frameworks.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)A agricultura de precisão se beneficiou muito das novas tecnologias ao longo dos anos. O uso de sensores multiespectrais e hiperespectrais acoplados aos Veículos Aéreos Não Tripulados (VANT) permitiu que as fazendas monitorassem as lavouras, melhorassem o uso de recursos e reduzissem os custos. Apesar de amplamente utilizadas, as imagens multiespectrais apresentam um desalinhamento natural entre os vários espectros devido ao uso de diferentes sensores. A variação do espectro analisado também leva à perda de características entre as bandas, o que dificulta o processo de detecção de atributos entre as bandas, o que torna complexo o processo de alinhamento. Neste trabalho, propomos um novo framework para o processo de alinhamento entre as bandas com base em duas premissas: i) o desalinhamento natural é um atributo da câmera, e por esse motivo ele não é alterado durante o processo de aquisição; ii) a velocidade de deslocamento do VANT, quando comparada à velocidade entre a aquisição da primeira e a última banda, não é suficiente para criar distorções significativas. Os resultados obtidos foram comparados com o padrão ouro gerado por um especialista e com outros métodos presentes na literatura. O framework proposto teve um back-projection error (BP) de 0, 425 pixels, sendo este resultado 335% melhor aos frameworks avaliados

Wavelet-Based Genomic Signal Processing for Centromere Identification and Hypothesis Generation

Various ‘omics data types have been generated for Populus trichocarpa, each providing a layer of information which can be represented as a density signal across a chromosome. We make use of genome sequence data, variants data across a population as well as methylation data across 10 different tissues, combined with wavelet-based signal processing to perform a comprehensive analysis of the signature of the centromere in these different data signals, and successfully identify putative centromeric regions in P. trichocarpa from these signals. Furthermore, using SNP (single nucleotide polymorphism) correlations across a natural population of P. trichocarpa, we find evidence for the co-evolution of the centromeric histone CENH3 with the sequence of the newly identified centromeric regions, and identify a new CENH3 candidate in P. trichocarpa

A neuro-genetic hybrid approach to automatic identification of plant leaves

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification. This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves. A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection. This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented

Detection and Classification Techniques for Skin Lesion Images: A Review

Dermoscopy needs sophisticated and robust systems for successful treatment which would also help reduce the number of biopsies. Computer aided diagnosis of melanoma support clinical decision making which would provide relevant supporting evidence from the prior known cases to the dermatologists and practitioners and also ease the management of clinical data. These systems play an important role of an expert consultant by presenting cases that are not only similar in diagnosis but also similar in appearance and help in early detection and diagnosis of skin diseases. With the advances in technology, new algorithms have also been proposed to develop more efficient CAD systems. This article reviews various techniques that have been proposed for detection and classification of skin lesions

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Detecting Red Blood Cells Morphological Abnormalities Using Genetic Algorithm and Kmeans

Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. Computer-aided diagnosis is another important application of pattern recognition, aiming at assisting doctors in making diagnostic decisions. Many diseases which are not blood diseases in origin have hematological abnormalities and manifestation (have symptoms appeared on the blood). CBC (cell blood count test) for instance, is still the first test to be requested by the physicians or become in their mind. Blood abnormality can be in white blood cells, red blood cells and plasma. In this thesis, red blood cells are the suggested for detecting it is abnormality. The abnormality of blood cells shapes can't be detected easily, where the CBC (cell blood count) device give a count number and percentages not a description of the shapes of the blood cells, when the blood cells shapes wanted to be known, hematologist asked to view the blood films under the microscope which is time consuming task besides that the human error risk is high. Since the number of abnormal cells to normal cells in a given blood sample give a measure of the disease severity, detecting one cell with potential abnormality can give premature warning for future illness that can be avoided or treated earlier. This case can't be detected by hematologist. Computer involved in such task to save time and effort besides minimizing human error. This thesis name is "DETECTING RED BLOOD CELLS MORPHOLOGICAL ABNORMALITIES USING GENETIC ALGORITHM AND KMEANS". In this thesis, the thesis divided into four phases. First phase data collection where blood samples was drawn from healthy and sick people and then blood films made and viewed under microscope and an images captured for these blood films. Second phase preprocessing phase where the images prepared for the next phase. Third phase feature extraction was executed where these features are spatial domain and frequency domain features. Fourth phase is the classification phase where the features fed into the classifier to be classified. An acceptable detection rate is achieved by the proposed system. The genetic algorithm classifier success rate was 92.31% and the K-means classifier success rate was 94.00%