Contributions to texture analysis for digital image segmentation

Orientador: Marco Antonio Garcia de Carvalho, Paulo Sérgio Martins PedroDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: Segmentação é o processo de dividir a imagem em regiões ou grupos, que permitam a identificação de objetos ou características.Para isso, a área de Segmentação de Imagens possui uma gama de métodos que segmentam os mais diversos tipos de imagens. Dentre estes, existem os que empregam a análise de padrões de textura; análise esta que viabiliza a interpretação de diversas informações. As informações de textura são extraídas, normalmente, pixel a pixel. Extraí-las de regiões pré-definidas ainda é um desafio e necessita, em alguns casos, adaptar o descritor para este fim. Este trabalho apresenta duas contribuições referentes à análise de textura no processo de segmentação de imagens. A primeira consiste na segmentação de estômatos através da combinação da Transformada Wavelet à trous e a Transformada Watershed. A segunda consiste na aplicação da Matriz de Coocorrência, juntamente com a Transformada Watershed e o Corte Normalizado, para a segmentação de imagens naturais. As bases de dados utilizadas foram, respectivamente, uma base obtida juntamente com o grupo ScianLab da Universidade do Chile e a BSDS500, da Universidade da California-Berkeley. Os resultados em ambas as aplicações foram avaliados pela medida-F, amplamente utilizada na literatura, e comparados a situações que incluem diferentes técnicas para abordar a segmentação e o uso de textura. A performance das técnicas propostas foram bastante promissoras, como na primeira aplicação, com a obtenção de 98% de acurácia na identificação e 70% na segmentação dos estômatos e, para a segunda, na superação da acurácia de outras técnicasAbstract: Image segmentation is the process of splitting images into regions or groups that allows the identification of features and objects. The image segmentation field has several methods that segment the most diverse types of images. Among them, there are those that employ analysis of texture patterns, which facilitates the interpretation of relevant information in an image. Texture information is commonly extracted pixel by pixel from an image. Extraction from predefined regions remains a challenge that often requires the adaptation of texture descriptors. This work offers two contributions to image segmentation using texture analysis. The first is to segment stomata images through the combination of the \textit{à trous} Wavelet and the Watershed transforms. The second consists in the combination of the Gray-Level Coocurrence Matrix, the Watershed Transform and Normalized Cut to segment general images.The datasets were obtained from the ScianLab group (University of Chile) and the BSDS500 (University of California-Berkeley), respectively. The findings in both applications were evaluated by the well-known F-measure. The comparisons include different techniques to approach both segmentation and texture features. The results obtained were promising in both cases. For example, the first application achieved an accuracy of 98% and 70% in the identification and segmentation of stomata structures, respectively. In the second application, the F-measure accuracy outperformed other techniquesMestradoSistemas de Informação e ComunicaçãoMestre em Tecnologi

Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity

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

Meningioma classification using an adaptive discriminant wavelet packet transform

Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining to the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for selecting the most useful subbands and hence, achieves feature selection. It also provides a mechanism for ranking features based upon the discrimination power of a subband. The more discriminant a subband, the better it is for classification. The results show that high classification accuracies are obtained by selecting subbands with high discrimination power. Moreover, subbands that are more stable i.e. have a higher probability of being selected provide better classification accuracies. Stability and discrimination power have been shown to have a direct relationship with classification accuracy. Hence, ADWPT acquires a subset of subbands that provide a highly discriminant and robust set of features for Meningioma subtype classification. Classification accuracies obtained are greater than 90% for most Meningioma subtypes. Consequently, ADWPT is a robust and adaptive technique which enables it to overcome the issue of high intra-class variation by statistically selecting the most useful subbands for meningioma subtype classification. It overcomes the issue of low inter-class variation by adapting to texture samples and extracting the subbands that are best for differentiating between the various meningioma subtype textures

Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic

Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images

[Abstract] Diabetic Retinopathy and Diabetic Macular Edema (DME) represent one of the main causes of blindness in developed countries. They are characterized by fluid deposits in the retinal layers, causing a progressive vision loss over the time. The clinical literature defines three DME types according to the texture and disposition of the fluid accumulations: Cystoid Macular Edema (CME), Diffuse Retinal Thickening (DRT) and Serous Retinal Detachment (SRD). Detecting each one is essential as, depending on their presence, the expert will decide on the adequate treatment of the pathology. In this work, we propose a robust detection and visualization methodology based on the analysis of independent image regions. We study a complete and heterogeneous library of 375 texture and intensity features in a dataset of 356 labeled images from two of the most used capture devices in the clinical domain: a CIRRUSTM HD-OCT 500 Carl Zeiss Meditec and 179 OCT images from a modular HRA + OCT SPECTRALIS® from Heidelberg Engineering, Inc. We extracted 33,810 samples for each type of DME for the feature analysis and incremental training of four different classifier paradigms. This way, we achieved an 84.04% average accuracy for CME, 78.44% average accuracy for DRT and 95.40% average accuracy for SRD. These models are used to generate an intuitive visualization of the fluid regions. We use an image sampling and voting strategy, resulting in a system capable of detecting and characterizing the three types of DME presenting them in an intuitive and repeatable way.Xunta de Galicia; ED431G 2019/01This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia, Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project, Ayudas para la formación de profesorado universitario (FPU), grant ref. FPU18/02271; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%)

Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images

Diabetic Retinopathy and Diabetic Macular Edema (DME) represent one of the main causes of blindness in developed countries. They are characterized by fluid deposits in the retinal layers, causing a progressive vision loss over the time. The clinical literature defines three DME types according to the texture and disposition of the fluid accumulations: Cystoid Macular Edema (CME), Diffuse Retinal Thickening (DRT) and Serous Retinal Detachment (SRD). Detecting each one is essential as, depending on their presence, the expert will decide on the adequate treatment of the pathology. In this work, we propose a robust detection and visualization methodology based on the analysis of independent image regions. We study a complete and heterogeneous library of 375 texture and intensity features in a dataset of 356 labeled images from two of the most used capture devices in the clinical domain: a CIRRUSTM HD-OCT 500 Carl Zeiss Meditec and 179 OCT images from a modular HRA + OCT SPECTRALIS(R) from Heidelberg Engineering, Inc. We extracted 33,810 samples for each type of DME for the feature analysis and incremental training of four different classifier paradigms. This way, we achieved an 84.04% average accuracy for CME, 78.44% average accuracy for DRT and 95.40% average accuracy for SRD. These models are used to generate an intuitive visualization of the fluid regions. We use an image sampling and voting strategy, resulting in a system capable of detecting and characterizing the three types of DME presenting them in an intuitive and repeatable way

Texture analysis of multimodal magnetic resonance images in support of diagnostic classification of childhood brain tumours

Primary brain tumours are recognised as the most common form of solid tumours in children, with pilocytic astrocytoma, medulloblastoma and ependymoma being found most frequently. Despite their high mortality rate, early detection can be facilitated through the use of Magnetic Resonance Imaging (MRI), which is the preferred scanning technique for paediatric patients. MRI offers a variety of imaging sequences through structural and functional imaging, as well as providing complementary tissue information. However visual examination of MR images provides limited ability to characterise distinct histological types of brain tumours. In order to improve diagnostic classification, we explore the use of a computer-aided system based on texture analysis (TA) methods. TA has been applied on conventional MRI but has been less commonly studied on diffusion MRI of brain-related pathology. Furthermore, the combination of textural features derived from both imaging approaches has not yet been widely studied. In this thesis, the aim of the research is to investigate TA based on multi-centre multimodal MRI, in order to provide more comprehensive information and develop an automated processing framework for the classification of childhood brain tumours