Fundamental Shape Discrimination of Underground Metal Object Through One-Axis Ground Penetrating Radar (GPR) Scan

Ground Penetrating Radar (GPR) was used in this research to detect or recognize the buried objects underground. Hyperbolic signals formed by datagram of GPR after detection the buried objects which quite similar to each other in term of metal shapes. The research was tested on the metal cube and metal cylinder by using the A-scan of GPR. There are steps in this signal processing step which are pre-processing step, feature extraction, and classification process. The segmentation process hyperbolic signals were segmented one by one and normalize from the negative to positive signals. The hyperbole from the metal cylinder and metal cube that had been buried in the ground is differentiated using four features of their respective A-scans which are found the maximum value of amplitude signal graph, the number of peaks in the signals graph, skewness, and standard deviation values. Finally, the classification process used learning algorithm of Multi-Layer Perceptron (MLP) was a test on Bayesian Regulation Backpropagation (BR) was given the highest accuracy, 98.70% as a classifier to classify the metal shapes which are a metal cube and metal cylinder

Automated Detection of Acute Leukemia using K-mean Clustering Algorithm

Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.Comment: Presented in ICCCCS 201

Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training

Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network. The goal of this research is to improve the neural network training algorithms and apply and test them in classification and recognition problems. In this paper, we describe a method of applying Bayesian regularization to improve Levenberg-Marquardt (LM) algorithm and make it better usable in training neural networks. In the experimental part, we qualify the modified LM algorithm using Bayesian regularization and use it to determine an appropriate number of hidden layers in the network to avoid overtraining. The result of the experiment was very encouraging with a 98.8% correct classification when run on test samples

Automated cell counting system for chronic leukemia

Leukemia is a group of cancers which create a large amount of immature white blood cells. Abnormal numbers of white blood cells may suggest a screening of leukemia, and the blood sample is examined under the microscope to observe if the cells appear abnormal. The manual screening of chronic leukemia is time consuming and tedious while the Automated Hematology Analyzer is too expensive, particularly for the third world countries. This has been made exacerbated by the gold standard of biopsy inspiration which is painful and invasive for the patient. An automated cell counting (ACC) system for chronic leukemia has been developed to support and ease the routine of hematologist and technologist in the screening process and to give a quick and accurate result. The fusion of image processing technique has been proposed, which include four main stages, i.e. image acquisition, image segmentation, noise removal and counting process. Based on the sensitivity test over 100 images of chronic cells, an overall result shows 98.94% sensitivity of the system performance and the processing time recorded is less than 6 second per image. This proved an excellent level of ACC system performance. It is concluded that the system is suitable to be used as an automated counting system for chronic leukemia disease due to its sensitivity and ability to reduce the time taken for screening process

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images

Morphological analysis is the starting point for the diagnostic approach of more than 80% of the hematological diseases. However, the morphological differentiation among different types of abnormal lymphoid cells in peripheral blood is a difficult task, which requires high experience and skill. Objective values do not exist to define cytological variables, which sometimes results in doubts on the correct cell classification in the daily hospital routine. Automated systems exist which are able to get an automatic preclassification of the normal blood cells, but fail in the automatic recognition of the abnormal lymphoid cells. The general objective of this thesis is to develop a complete methodology to automatically recognize images of normal and reactive lymphocytes, and several types of neoplastic lymphoid cells circulating in peripheral blood in some mature B-cell neoplasms using digital image processing methods. This objective follows two directions: (1) with engineering and mathematical background, transversal methodologies and software tools are developed; and (2) with a view towards the clinical laboratory diagnosis, a system prototype is built and validated, whose input is a set of pathological cell images from individual patients, and whose output is the automatic classification in one of the groups of the different pathologies included in the system. This thesis is the evolution of various works, starting with a discrimination between normal lymphocytes and two types of neoplastic lymphoid cells, and ending with the design of a system for the automatic recognition of normal lymphocytes and five types of neoplastic lymphoid cells. All this work involves the development of a robust segmentation methodology using color clustering, which is able to separate three regions of interest: cell, nucleus and peripheral zone around the cell. A complete lymphoid cell description is developed by extracting features related to size, shape, texture and color. To reduce the complexity of the process, a feature selection is performed using information theory. Then, several classifiers are implemented to automatically recognize different types of lymphoid cells. The best classification results are achieved using support vector machines with radial basis function kernel. The methodology developed, which combines medical, engineering and mathematical backgrounds, is the first step to design a practical hematological diagnosis support tool in the near future.Los análisis morfológicos son el punto de partida para la orientación diagnóstica en más del 80% de las enfermedades hematológicas. Sin embargo, la clasificación morfológica entre diferentes tipos de células linfoides anormales en la sangre es una tarea difícil que requiere gran experiencia y habilidad. No existen valores objetivos para definir variables citológicas, lo que en ocasiones genera dudas en la correcta clasificación de las células en la práctica diaria en un laboratorio clínico. Existen sistemas automáticos que realizan una preclasificación automática de las células sanguíneas, pero no son capaces de diferenciar automáticamente las células linfoides anormales. El objetivo general de esta tesis es el desarrollo de una metodología completa para el reconocimiento automático de imágenes de linfocitos normales y reactivos, y de varios tipos de células linfoides neoplásicas circulantes en sangre periférica en algunos tipos de neoplasias linfoides B maduras, usando métodos de procesamiento digital de imágenes. Este objetivo sigue dos direcciones: (1) con una orientación propia de la ingeniería y la matemática de soporte, se desarrollan las metodologías transversales y las herramientas de software para su implementación; y (2) con un enfoque orientado al diagnóstico desde el laboratorio clínico, se construye y se valida un prototipo de un sistema cuya entrada es un conjunto de imágenes de células patológicas de pacientes analizados de forma individual, obtenidas mediante microscopía y cámara digital, y cuya salida es la clasificación automática en uno de los grupos de las distintas patologías incluidas en el sistema. Esta tesis es el resultado de la evolución de varios trabajos, comenzando con una discriminación entre linfocitos normales y dos tipos de células linfoides neoplásicas, y terminando con el diseño de un sistema para el reconocimiento automático de linfocitos normales y reactivos, y cinco tipos de células linfoides neoplásicas. Todo este trabajo involucra el desarrollo de una metodología de segmentación robusta usando agrupamiento por color, la cual es capaz de separar tres regiones de interés: la célula, el núcleo y la zona externa alrededor de la célula. Se desarrolla una descripción completa de la célula linfoide mediante la extracción de descriptores relacionados con el tamaño, la forma, la textura y el color. Para reducir la complejidad del proceso, se realiza una selección de descriptores usando teoría de la información. Posteriormente, se implementan varios clasificadores para reconocer automáticamente diferentes tipos de células linfoides. Los mejores resultados de clasificación se logran utilizando máquinas de soporte vectorial con núcleo de base radial. La metodología desarrollada, que combina conocimientos médicos, matemáticos y de ingeniería, es el primer paso para el diseño de una herramienta práctica de soporte al diagnóstico hematológico en un futuro cercano

An intelligent decision support system for acute lymphoblastic leukaemia detection

The morphological analysis of blood smear slides by haematologists or haematopathologists is one of the diagnostic procedures available to evaluate the presence of acute leukaemia. This operation is a complex and costly process, and often lacks standardized accuracy owing to a variety of factors, including insufficient expertise and operator fatigue. This research proposes an intelligent decision support system for automatic detection of acute lymphoblastic leukaemia (ALL) using microscopic blood smear images to overcome the above barrier. The work has four main key stages. (1) Firstly, a modified marker-controlled watershed algorithm integrated with the morphological operations is proposed for the segmentation of the membrane of the lymphocyte and lymphoblast cell images. The aim of this stage is to isolate a lymphocyte/lymphoblast cell membrane from touching and overlapping of red blood cells, platelets and artefacts of the microscopic peripheral blood smear sub-images. (2) Secondly, a novel clustering algorithm with stimulating discriminant measure (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of the nucleus and cytoplasm of lymphocytic cell membranes. The SDM measures are used in conjunction with Genetic Algorithm for the clustering of nucleus, cytoplasm, and background regions. (3) Thirdly, a total of eighty features consisting of shape, texture, and colour information from the nucleus and cytoplasm of the identified lymphocyte/lymphoblast images are extracted. (4) Finally, the proposed feature optimisation algorithm, namely a variant of Bare-Bones Particle Swarm Optimisation (BBPSO), is presented to identify the most significant discriminative characteristics of the nucleus and cytoplasm segmented by the SDM-based clustering algorithm. The proposed BBPSO variant algorithm incorporates Cuckoo Search, Dragonfly Algorithm, BBPSO, and local and global random walk operations of uniform combination, and Lévy flights to diversify the search and mitigate the premature convergence problem of the conventional BBPSO. In addition, it also employs subswarm concepts, self-adaptive parameters, and convergence degree monitoring mechanisms to enable fast convergence. The optimal feature subsets identified by the proposed algorithm are subsequently used for ALL detection and classification. The proposed system achieves the highest classification accuracy of 96.04% and significantly outperforms related meta-heuristic search methods and related research for ALL detection

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

Genetic algorithm-neural network : feature extraction for bioinformatics data

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Caracterització morfològica de cèl·lules limfoides normals, reactives, anormals i blàstiques de sang perifèrica mitjançant processament digital d'imatges

Aplicat embargament des de la data de defensa fins a l'abril de 2020The main objective of the present Doctoral Thesis is to obtain new quantitative features by means of digital image processing and machine learning for the differentiation of normal, reactive and malignant lymphoid cells of peripheral blood, contributing to an objective morphologic assessment.The research addresses the following two issues:(1)Using machine learning, geometric, color and texture descriptors are searched which have an explicit quantitative formulation and a reasonable qualitative interpretation in visual morphologic terms.(2)Considering cellular abnormalities established a priori, associated with specific diseases, the aim is to identify specific quantitative descriptors of morphological characteristics that cytologists recognize visually and usually express subjectively.More than 200 patients and 16 different lymphoid cell groups have been included in the research. Using the CellaVision DM96, 12,000 images have been acquired and using the microscope Olympus BX43, 9,000. Almost 2,700 features including geometric, color and texture (first and second-order statistics, granulometric, Wavelet and Gabor) have been analyzed for lymphoid cell differentiation. Six color spaces have been considered: RGB, CMYK, HSV, XYZ, Lab and Luv.The 20 most efficient features for the differentiation between reactive lymphoid cells (infections) and neoplastic cells (abnormal lymphocytes in lymphoma or lymphoid blasts in acute leukemia) have been analyzed. The three most relevant descriptors for the recognition of the 12 lymphoid cell groups considered are geometric: 1) nucleus/cytoplasm ratio, 2) nuclear perimeter and 3) cell diameter. Most of the 20 descriptors show significant differences between pairs of abnormal lymphocytes which are difficult to recognize by morphology. Five color and texture features are significant to discriminate reactive lymphocytes from abnormal lymphoid cells.Quantitative descriptors have been identified for the detection of specific cell morphologic abnormalities of certain lymphoid neoplasms, which have shown good specificity and sensitivity using the two different image acquisition systems. Regarding nuclear abnormalities, the detection of the mature and condensed chromatin seen in chronic lymphatic leukemia cells has been achieved by the correlation of the cyan of the nucleus, and the cerebriform chromatin characteristic of Sézary cells, by means of the standard deviation of the granulometric curve of the cyan component of the nucleus.Regarding the cytoplasm, hairiness descriptor has been able to detect cytoplasmic villi present in villous lymphocytes in hairy cell leukemia and splenic marginal zone lymphoma. The skewness of the histogram of the u component of the cytoplasm has shown to be useful for detecting azurophilic cytoplasmic granules seen in abnormal lymphocytes in T-cell large granular lymphocytic leukemia.The results of this Doctoral Thesis provide objectivity in the morphologic assessment of normal, reactive and neoplastic lymphoid cells. Obtaining quantitative descriptors for abnormal lymphoid cells, which are specific to certain lymphoid neoplasms with peripheral blood expression, could facilitate their detection. Hematological analyzers based on digital image analysis could benefit from the use of quantitative descriptors, such as those described herein, in order to discriminate between reactive and neoplastic lymphoid cells.L'objectiu general de la present Tesi és obtenir nous descriptors quantitatius mitjançant processament digital d’imatges i aprenentatge automàtic per a la diferenciació de cèl·lules limfoides normals, reactives i malignes de sang perifèrica, contribuint a una anàlisi objectiva de la citologia sanguínia. La recerca s'ha enfocat des de dues perspectives: (1) Partint de l'aprenentatge automàtic, s'han buscat descriptors geomètrics, de color i de textura que tinguin una formulació quantitativa explícita i una interpretació qualitativa raonable en termes morfològics visuals. (2) Partint d'anormalitats cel·lulars establertes a priori, associades a malalties específiques, l'objectiu és identificar descriptors quantitatius específics de característiques morfològiques que els citòlegs reconeixen de forma visual i expressen habitualment amb conceptes subjectius. S'han inclòs més de 200 pacients i 16 grups cel·lulars limfoides. Amb el sistema CellaVision DM96 s'han adquirit 12.000 imatges i amb el microscopi Olympus BX43, 9.000. Per a la diferenciació cel·lular s’han analitzat 2.700 descriptors geomètrics, de color i de textura (estadístics de primer i segon ordre, granulomètrics, Wavelet i Gabor). S'han considerat sis espais de color (RGB, CMGN, HSV, XYZ, Lab i Luv). S'han analitzat els 20 descriptors geomètrics, de color i de textura més eficients per a la diferenciació entre cèl·lules limfoides reactives (infeccions) i neoplàsiques (anormals als limfomes o blasts limfoides a les leucèmies agudes). Els descriptors més rellevants per al reconeixement dels 12 grups cel·lulars limfoides són geomètrics: 1) relació nucli/citoplasma, 2) perímetre del nucli i 3) diàmetre de la cèl·lula. La majoria dels 20 descriptors mostren diferències significatives entre parelles de limfòcits anormals de difícil reconeixement per morfologia. Cinc descriptors de color i textura són significatius per discriminar els limfòcits reactius dels anormals. S'han identificat descriptors quantitatius per a la detecció d'anomalies morfològiques específiques de cèl·lules de determinades neoplàsies limfoides, que han mostrat una bona especificitat i sensibilitat amb els dos sistemes diferents d’adquisició d’imatges. En relació a anormalitats nuclears, la detecció de la cromatina madura i condensada de les cèl·lules de la leucèmia limfàtica crònica ha estat possible mitjançant la correlació del cian del nucli, i de la cromatina cerebriforme característica de les cèl·lules de Sézary mitjançant la desviació estàndard de la corba granulomètrica del component cian del nucli. Pel que fa al citoplasma, s'han detectat les prolongacions citoplasmàtiques dels limfòcits de la tricoleucèmia i del limfoma de la zona marginal esplènic mitjançant el descriptor hairiness. L'asimetria de l'histograma del component u del citoplasma ha demostrat ser útil per detectar els grànuls azuròfils dels limfòcits grans granulars. Els resultats de la present Tesi proporcionen objectivitat en l'avaluació morfològica de cèl·lules limfoides normals, reactives i neoplàsiques. L'obtenció de descriptors quantitatius per cèl·lules limfoides anormals específiques de determinades neoplàsies limfoides amb expressió a sang perifèrica podria facilitar la seva detecció. Els analitzadors hematològics basats en l'anàlisi digital d'imatges es podrien beneficiar de l'ús de descriptors quantitatius, com els descrits, per tal de discriminar entre cèl·lules limfoides reactives i neoplàsiques.Postprint (published version