Analysis of Scanning Acoustic Microscopy Images of IC Chips

The detection, isolation, and characterization of flaws in components represent a critical need in manufacturing and quality control. Nondestructive testing (NDT) provides an effective way of inspecting materials for ensuring the quality and integrity of products and systems. Consequently, nondestructive inspection finds extensive application in several industries such as steel, nuclear and electronic industries for the evaluation of complex test objects with minimal interruption of routine operations[1].</p

A novel computer vision based neutrosophic approach for leaf disease identification and classification

The natural products are inexpensive, non-toxic, and have fewer side effects. Thus, their demand especially herbs based medical products, health products, nutritional supplements, cosmetics etc. are increasing. The quality of leafs defines the degree of excellence or a state of being free from defects, deficits, and substantial variations. Also, the diseases in leafs possess threats to the economic, and production status in the agricultural industry worldwide

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Методы обработки и анализа изображений диагностических кристаллограмм

В учебном пособии рассмотрены современные технологии анализа диагностических изображений кристаллов биологических жидкостей. Изложение математических моделей и методов анализа ориентировано на студентов старших курсов и аспирантов, при этом основное внимание уделяется изучению новых информационных технологий, инструментальных методик и технических средств, ориентированных на решение прикладных задач анализа биомедицинских изображений. В учебном пособии рассмотрены методы текстурного анализа, в частности, приведены методы формирования статистических текстурных признаков. В учебном пособии исследуется информативность признаков на основе методов дискриминантного анализа. Рассматриваются методы формирования медико-диагностических признаков кристаллограмм на основе оценивания их геометрических параметров. При этом используется теория метода поля направлений, спектральные методы. Учебное пособие предназначено для проведения лекционных и лабораторных занятий по курсу "Математические методы обработки изображений" дляГриф.Используемые программы: Adobe Acrobat.Труды сотрудников СГАУ (электрон. версия)

Variations and Application Conditions Of the Data Type »Image« - The Foundation of Computational Visualistics

Few years ago, the department of computer science of the University Magdeburg invented a completely new diploma programme called 'computational visualistics', a curriculum dealing with all aspects of computational pictures. Only isolated aspects had been studied so far in computer science, particularly in the independent domains of computer graphics, image processing, information visualization, and computer vision. So is there indeed a coherent domain of research behind such a curriculum? The answer to that question depends crucially on a data structure that acts as a mediator between general visualistics and computer science: the data structure "image". The present text investigates that data structure, its components, and its application conditions, and thus elaborates the very foundations of computational visualistics as a unique and homogenous field of research. Before concentrating on that data structure, the theory of pictures in general and the definition of pictures as perceptoid signs in particular are closely examined. This includes an act-theoretic consideration about resemblance as the crucial link between image and object, the communicative function of context building as the central concept for comparing pictures and language, and several modes of reflection underlying the relation between image and image user. In the main chapter, the data structure "image" is extendedly analyzed under the perspectives of syntax, semantics, and pragmatics. While syntactic aspects mostly concern image processing, semantic questions form the core of computer graphics and computer vision. Pragmatic considerations are particularly involved with interactive pictures but also extend to the field of information visualization and even to computer art. Four case studies provide practical applications of various aspects of the analysis

Visual Speech Recognition

In recent years, Visual speech recognition has a more concentration, by researchers, than the past. Because of the leakage of the visual processing of the Arabic vocabularies recognition, we start to search in this field. Audio speech recognition concerned with the acoustic characteristic of the signal, but there are many situations that the audio signal is weak of not exist, and this will be a point in Chapter 2. The visual recognition process focuses on the features extracted from video of the speaker. These features are to be classified using several techniques. The most important feature to be extracted is motion. By segmenting motion of the lips of the speaker, an algorithm has manipulate it in such away to recognize the word which is said. But motion segmentation is not the only problem facing the speech recognition process, segmenting the lips itself is an early step in the speech recognition process, so, to segment lips motion we have to segment lips first, a new approach for lip segmentation is proposed in this thesis. Sometimes, motion feature needs another feature to support in recognition the spoken word. So in our thesis another new algorithm is proposed to use motion segmentation by using the Abstract Difference Image from an image series, supported by correlation for registering images in the image series, to recognize ten words in the Arabic language, the words are from “one” to “ten” in Arabic language. The algorithm also uses the HU-Invariant set of features to describe the Abstract Difference Image, and uses a three different recognition methods to recognize the words. The CLAHE method as a filtering technique is used by our algorithm to manipulate lighting problems. Our algorithm based on extracting the differences details from a series of images to recognize the word, achieved an overall results 55.8%, it is an adequate result for our algorithm when integrated in an audio-visual system

An Investigation of Global and Local Radiomic Features for Customized Self-Assessment Mammographic Test Sets for Radiologists in China in Comparison with Those in Australia

Self-assessment test sets have demonstrated being effective tools to improve radiologists’ diagnostic skills through immediate error feedback. Current sets use a one-size-fits-all approach in selecting challenging cases, overlooking cohort-specific weaknesses. This thesis assessed feasibility of using a comprehensive set of handcrafted global radiomic features (Stage 1, Chapter 3) as well as handcrafted (Stage 2, Chapter 4) and deep-learning based (Stage 3, Chapter 5) local radiomic features to identify challenging mammographic cases for Chinese and Australian radiologists. In the first stage, global handcrafted radiomic features and Random Forest models analyzed mammography datasets involving 36 radiologists from China and Australia independently assessing 60 dense mammographic cases. The results were used to build and evaluate models’ performance in case difficulty prediction. The second stage focused on local handcrafted radiomic features, utilizing the same dataset but extracting features from error-related local mammographic areas to analyze features linked to diagnostic errors. The final stage introduced deep learning, specifically Convolutional Neural Network (CNN), using an additional test set and radiologists’ readings to identify features linked to false positive errors. Stage 1 found that global radiomic features effectively detected false positive and false negative errors. Notably, Australian radiologists showed less predictable errors than their Chinese counterparts. Feature normalization did not improve model performance. In Stage 2, the model showed varying success rates in predicting false positives and false negatives among the two cohorts, with specific mammographic regions more prone to errors. In Stage 3, the transferred ResNet-50 architecture performed the best for both cohorts. In conclusion, the thesis affirmed the importance of radiomic features in improving curation of cohort-specific self-assessment mammography test sets

Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature