Mammography is used to aid early detection and diagnosis systems. It takes an x-ray image of the breast and can provide a second opinion for radiologists. The earlier detection is made, the better treatment works. Digital mammograms are dealt with by Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in a mammogram. The purpose of this study is to investigate how to categories cropped regions of interest (ROI) from digital mammogram images into two classes; normal and abnormal regions (which contain microcalcifications). The work proposed in this thesis is divided into three stages to provide a concept system for classification between normal and abnormal cases. The first stage is the Segmentation Process, which applies thresholding filters to separate the abnormal objects (foreground) from the breast tissue (background). Moreover, this study has been carried out on mammogram images and mainly on cropped ROI images from different sizes that represent individual microcalcification and ROI that represent a cluster of microcalcifications. The second stage in this thesis is feature extraction. This stage makes use of the segmented ROI images to extract characteristic features that would help in identifying regions of interest. The wavelet transform has been utilized for this process as it provides a variety of features that could be examined in future studies. The third and final stage is classification, where machine learning is applied to be able to distinguish between normal ROI images and ROI images that may contain microcalcifications. The result indicated was that by combining wavelet transform and SVM we can distinguish between regions with normal breast tissue and regions that include microcalcifications.

The number of women diagnosed with the breast cancer continues to rise year on year. Breast cancer is now the most common type of cancer in the UK, with over 55000 cases reported last year. In most cases, mammography is the first step towards diagnosing breast cancer. However, it continues to have many practical limitations as compared to more sophisticated modalities such as MRI. The relatively low cost of mammography, together with the ever increasing risk of women contracting the disease, has led to many developed countries having a breast screening program. These routine breast screens are taken at different points in time and are called temporal mammograms. Currently, a radiologist tends to qualitatively assess temporal mammograms and look for any abnormalities or suspicious regions that might be of a concern. In this thesis, we develop an automatic shape analysis model that can detect and quantify such changes inside the breast. This will not only help in early diagnosis of the disease, which is key to survival, but will potentially aid prognosis and post treatment care. The core to this thesis is the use of Circular Integral Invariants. We explore its multi-scale properties and use it for image smoothing to reduce image noise and enhance features for segmentation. We implement, modify and enhance a segmentation method which previously has been successfully used to acquire breast regions of interest. We applied such Integral Invariants for shape description, to be used for shape matching as well as for subdividing shapes into sub-regions and quantifying the differences between two such shapes. We combine boundary information with the information from inside a shape, thus eccentrically transforming shapes before describing their structure. We develop a novel false positives reduction method based on Integral Invariants scale space. A second aspect of the thesis is the evaluation of and emphasis on the use of breast density maps against the commonly used intensity maps or x-rays. We find density maps sufficient to use in clinical practice. The methods developed in this thesis aim to help clinicians in making diagnostic decision at the point of case. Our shape analysis model is easy to compute, fast and very general in nature that could be deployed in a wide range of applications, beyond mammography.

Durante o processo de usinagem, o conhecimento da temperatura é um dos fatores mais importante na análise do estado da ferramenta. Permite o controle dos fatores mais importantes que influenciam, no uso, na vida e no desgaste da ferramenta. A temperatura na região de contato entre a peça e a ferramenta é resultante do processo de remoção de material durante a operação de corte e é difícil de se obter uma vez que, ou a peça, ou a ferramenta estão em movimento. Uma maneira de se medir a temperatura nessa situação é detectando a radiação de infravermelho. Este trabalho tem objetivo de apresentar uma nova metodologia de diagnóstico e monitoramento de operações de usinagem com o uso de imagens de infravermelho. A imagem de infravermelho fornece um mapa em tons de cinza da temperatura dos elementos participantes do processo: ferramenta, peça e cavaco. Cada tom de cinza na imagem corresponde a uma temperatura para cada material. A correspondência entre tons de cinza e a temperatura é dada pela prévia calibração da câmera de infravermelho para os materiais participantes do processo. O sistema desenvolvido neste trabalho usa uma câmera de infravermelho, uma frame grabber e um software composto por 3 módulos: o primeiro módulo faz a aquisição da imagem de infravermelho e o processamento; o segundo módulo faz a extração e o cálculo do vetor de características das imagens. Finalmente o terceiro módulo usa um algoritmo fuzzy e fornece como saída o diagnóstico do estado da ferramenta. / During machining process the temperature knowledge is one of the most important factors in tool analysis. It allows to control main factors that influence tool use, life time and wear. The temperature in the contact area between the work piece and the tool is resulting from the material remova! in cutting operation and it is too difficult to be obtained because the tool, or the work piece is in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements temperature in the process: tool, work piece and chips. Each gray tone corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is goven by previous infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed by three modules. The first module provides the image acquisition and processing. The second one does the image feature extraction and calculates the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and to supply the tool state diagnostic as output.

Medical imaging has advanced at a tremendous rate since x-rays were discovered in 1895. Today, x-ray machines produce extremely high-quality images for radiologists to interpret. However, the methods of interpretation have only recently begun to be augmented by advances in computer technology. Computer aided diagnosis (CAD) systems that guide healthcare professionals to making the correct diagnosis are slowly becoming more prevalent throughout the medical field. Bone fractures are a relatively common occurrence. In most developed countries the number of fractures associated with age-related bone loss is increasing rapidly. Regardless of the treating physician's level of experience, accurate detection and evaluation of musculoskeletal trauma is often problematic. Each year, the presence of many fractures is missed during x-ray diagnosis. For a trauma patient, a mis-diagnosis can lead to ineffective patient management, increased dissatisfaction, and expensive litigation. As a result, detection of long-bone fractures is an important orthopaedic and radiologic problem, and it is proposed that a novel CAD system could help lower the miss rate. This thesis examines the development of such a system, for the detection of long-bone fractures. A number of image processing software algorithms useful for automating the fracture detection process have been created. The first algorithm is a non-linear scale-space smoothing technique that allows edge information to be extracted from the x-ray image. The degree of smoothing is controlled by the scale parameter, and allows the amount of image detail that should be retained to be adjusted for each stage of the analysis. The result is demonstrated to be superior to the Canny edge detection algorithm. The second utilises the edge information to determine a set of parameters that approximate the shaft of the long-bone. This is achieved using a modified Hough Transform, and specially designed peak and line endpoint detectors. The third stage uses the shaft approximation data to locate the bone centre-lines and then perform diaphysis segmentation to separate the diaphysis from the epiphyses. Two segmentation algorithms are presented and one is shown to not only produce better results, but also be suitable for application to all long-bone images. The final stage applies a gradient based fracture detection algorithm to the segmented regions. This algorithm utilises a tool called the gradient composite measure to identify abnormal regions, including fractures, within the image. These regions are then identified and highlighted if they are deemed to be part of a fracture. A database of fracture images from trauma patients was collected from the emergency department at the Flinders Medical Centre. From this complete set of images, a development set and test set were created. Experiments on the test set show that diaphysis segmentation and fracture detection are both performed with an accuracy of 83%. Therefore these tools can consistently identify the boundaries between the bone segments, and then accurately highlight midshaft long-bone fractures within the marked diaphysis. Two of the algorithms---the non-linear smoothing and Hough Transform---are relatively slow to compute. Methods of decreasing the diagnosis time were investigated, and a set of parallelised algorithms were designed. These algorithms significantly reduced the total calculation time, making use of the algorithm much more feasible. The thesis concludes with an outline of future research and proposed techniques that---along with the methods and results presented---will improve CAD systems for fracture detection, resulting in more accurate diagnosis of fractures, and a reduction of the fracture miss rate.

The thesis develops a model (that includes a conceptual framework and an implementation) for analysing and classifying traditional X-ray images (MACXI) according to the severity of diseases as a Computer-Aided-Diagnosis tool with three initial objectives. � The first objective was to interpret X-ray images by transferring expert knowledge into a knowledge base (CXKB): to help medical staff to concentrate only on the interest areas of the images. � The second objective was to analyse and classify X-ray images according to the severity of diseases through the knowledge base equipped with an image processor (CXIP). � The third objective was to demonstrate the effectiveness and limitations of several image-processing techniques for analysing traditional chest X-ray images. A database was formed based on collection of expert diagnosis details for lung images. Five important features from lung images, as well as diagnosis rules were identified and simplified. The expert knowledge was transformed into a Knowledge base (KB) for analysing and classifying traditional X-ray images according to the severity of diseases (CXKB). Finally, an image processor named CXIP was developed to extract the features of lung images features and image classification. CXKB contains 63 distinct lung diseases with detailed descriptions. Some 80-chest X-ray images with diagnosis details were collected for the database from different sources, including online medical resources. A total of 61 images were used to determine the important features; 19 chest X-ray images were not used because of low visibility or the difficulty of diagnosis. Finally, only 12 images were selected after examining the diagnosis details, image clarity, image completeness, and image orientation. The most important features of lung diseases are a pattern of lesions with different levels of intensity or brightness. The other major anatomical structures of the chest are the hilum area, the rib area, the trachea area, and the heart area. Seven different severity levels of diseases were determined. Development and simplification of rules based on the image library were analysed, developed, and tested against the 12 images. A level of severity was labelled for each image based on a personal understanding of all the image and diagnosis details. Then, MACXI processed the selected 12 images to determine the level of severity. These 12 images were fed into the CXIP for recognition of the features and classification of the images to an accurate level of severity. Currently, the processor has the ability to identify diseased lung areas with approximately 80% success rate. A step by step demonstration of several image processing techniques that were used to build the processor is given to highlight the effectiveness and limitations of the techniques for analysing traditional chest X-ray images is also presented.

<p>Breast cancer screening is currently performed by mammography, which is limited by overlying anatomy and dense breast tissue. Computer aided detection (CADe) systems can serve as a double reader to improve radiologist performance. Tomosynthesis is a limited-angle cone-beam x-ray imaging modality that is currently being investigated to overcome mammography's limitations. CADe systems will play a crucial role to enhance workflow and performance for breast tomosynthesis.</p><p>The purpose of this work was to develop unique CADe algorithms for breast tomosynthesis reconstructed volumes. Unlike traditional CADe algorithms which rely on segmentation followed by feature extraction, selection and merging, this dissertation instead adopts information theory principles which are more robust. Information theory relies entirely on the statistical properties of an image and makes no assumptions about underlying distributions and is thus advantageous for smaller datasets such those currently used for all tomosynthesis CADe studies.</p><p>The proposed algorithm has two 2 stages (1) initial candidate generation of suspicious locations (2) false positive reduction. Images were accrued from 250 human subjects. In the first stage, initial suspicious locations were first isolated in the 25 projection images per subject acquired by the tomosynthesis system. Only these suspicious locations were reconstructed to yield 3D Volumes of Interest (VOI). For the second stage of the algorithm false positive reduction was then done in three ways: (1) using only the central slice of the VOI containing the largest cross-section of the mass, (2) using the entire volume, and (3) making decisions on a per slice basis and then combining those decisions using either a linear discriminant or decision fusion. A 92% sensitivity was achieved by all three approaches with 4.4 FPs / volume for approach 1, 3.9 for the second approach and 2.5 for the slice-by-slice based algorithm using decision fusion.</p><p>We have therefore developed a novel CADe algorithm for breast tomosynthesis. The techniques uses an information theory approach to achieve very high sensitivity for cancer detection while effectively minimizing false positives.</p> / Dissertation

This thesis is dealing with classifiers in Computer Aided Diagnosis in medical imaging. In particular, it focuses on artificial neural networks and feature selection methods. The specific goals of the thesis are: 1. Search for optimal topology of a feed-forward neural network (FFNN), dealing with four (4) medical imaging classification problems (Cytology, MRI, CT, and Mammography). 2. Study three (3) feature selection (dimensionality reduction) methods including PCA, stepwise analysis and t-test ranking for the FFNN topology defined in the previous step, for the four (4) medical imaging classification problems at hand. 3. Compare performance of the FFNN scheme to KNN, SVM, PNN and LDA classifiers, dealing with the above mentioned four (4) medical imaging classification problems. 10-fold cross validation estimation of generalization performance (generalization error) of the classification schemes was utilized. 4. Statistical significance of the results was validated utilizing ANOVA and Duncan’s test. To facilitate experimentation, a user-friendly application was developed (Chapter 3) that allows the user to find the best network topology on feature vectors, selected by various pre-processing techniques, and compared with other classifiers. The results of this are: 1. There is no statistical evidence that the different topology that is tested have any impact on classification performance of FFNN in any of the classification problem that this thesis is dealt off. 2. The stepwise method of dimensionality reduction (feature selection) is statistically significance better method than the other methods, except in the case of one dataset (Cytology) where there are no statistical significant differences. This is because of the inherent ability of stepwise method to select uncorrelated features unlike the other two methods (the datasets that the stepwise featured better performance had many highly correlated features). 3. There is no statistical significant better classifier in most cases, while neuronal classifier exhibits very good behaviour on all cases. For that reason, the selection of classifier does not seem to affect the classification problems at hand. Furthermore, the choice of classifier could be done based on other criteria than the classification performance, such as, the simplicity and plasticity, features that characterize the FFNN. / Το αντικείμενο αυτής της εργασίας είναι οι ταξινομητές στα συστήματα υποβοηθούμενης διάγνωσης στην ιατρική απεικόνιση. Ειδικότερα, εστιάζει στα τεχνητά νευρωνικά δίκτυα καθώς και σε μεθόδους επιλογής χαρακτηριστικών. Οι στόχοι αυτής της εργασίας είναι: 1. Η αναζήτηση της βέλτιστης τοπολογίας ενός πρόσω κατευθυντικού νευρωνικού δικτύου, σε τέσσερα (4) προβλήματα ταξινόμησης ιατρικής απεικόνισης (κυτταρολογία, μαγνητική απεικόνιση, αξονική τομογραφία και μαστογραφία). 2. Η μελέτη τριών (3) μεθόδων επιλογής χαρακτηριστικών (μείωσης διαστάσεων) συμπεριλαμβανομένων της ανάλυσης κύριων συνιστωσών, της σταδιακής αναζήτησης και της κατάταξης κατά τ-τέστ για τα τέσσερα (4) προβλήματα ταξινόμησης που είχαμε στη διάθεσή μας. 3. Η σύγκριση της απόδοσης του πρόσω κατευθυντικού νευρωνικού δικτύου (FFNN) με τους KNN, SVM, PNN και LDA ταξινομητές, στα τέσσερα (4) προαναφερθέντα ιατρικά προβλήματα ταξινόμησης. Για την εκτίμηση της απόδοσης γενίκευσης (σφάλμα γενίκευσης) χρησιμοποιήθηκε η 10-πτυχη διασταυρούμενη επικύρωση. 4. Η στατιστική σημαντικότητα των αποτελεσμάτων ελέγχθηκε με τις δοκιμασίες της ανάλυσης της διακύμανσης κατά ένα παράγοντα (ANOVA) και της δοκιμασίας Duncan. Για την διευκόλυνση του πειραματικού μέρους αναπτύχθηκε μια φιλική στο χρήστη εφαρμογή που επιτρέπει την αναζήτηση της βέλτιστης τοπολογίας του νευρωνικού δικτύου για τα επιλεγμένα χαρακτηριστικά, και τις προεπιλεγμένες τεχνικές προ-επεξεργασίας, ενώ επιτρέπει και την σύγκριση του με άλλους ταξινομητές. Τα αποτελέσματα του πειραματικού μέρους αυτής της εργασίας είναι: 1. Δεν αποδεικνύεται στατιστικά ότι η τοπολογία του δικτύου έχει κάποια επίδραση στην απόδοση του στα τέσσερα αυτά προβλήματα που μελετήθηκαν. 2. Η μέθοδος σταδιακής αναζήτησης είναι στατιστικά καλύτερη μέθοδος για επιλογή χαρακτηριστικών (τη μείωση των διαστάσεων), εκτός από το ένα πρόβλημα που αφορούσε στην κυτταρολογία όπου δεν αποδείχθηκε στατιστικά σημαντική διαφορά μεταξύ των μεθόδων. Αυτό οφείλεται στο γεγονός ότι η μέθοδος σταδιακής αναζήτησης έχει την «ενδογενή» ικανότητα να επιλέγει χαρακτηριστικά που είναι ανεξάρτητα μεταξύ τους με αποτέλεσμα την αυξημένη διακριτική ικανότητα του τελικού συνόλου (τα προβλήματα που η μέθοδος αυτή επέδειξε καλή συμπεριφορά είχαν χαρακτηριστικά με υψηλό βαθμό συσχέτισης). 3. Δεν αποδεικνύεται στατιστικά καλύτερος ταξινομητής στις περισσότερες περιπτώσεις ενώ ο νευρωνικός ταξινομητής επιδεικνύει πολύ καλή συμπεριφορά σε όλες τις περιπτώσεις. Για το λόγο αυτό, η επιλογή του ταξινομητή δεν φαίνεται να επηρεάζει σε σημαντικό βαθμό την απόδοση του συστήματος στα προβλήματα που έχουν μελετηθεί εδώ. Επιπλέον, η επιλογή του ταξινομητή μπορεί να γίνει με όρους διαφορετικούς από την ταξινομητική ικανότητά τους όπως απλότητα και ευελιξία, χαρακτηριστικά που έχει ο νευρωνικός ταξινομητής.

The goal of this research was to improve the speed and accuracy of reporting by clinical radiologists. By applying a technique known as eigenimage processing to chest radiographs, abnormal findings were enhanced and a classification scheme developed. Results confirm that the method is feasible for clinical use. Eigenimage processing is a popular face recognition routine that has only recently been applied to medical images, but it has not previously been applied to full size radiographs. Chest radiographs were chosen for this research because they are clinically important and are challenging to process due to their large data content. It is hoped that the success with these images will enable future work on other medical images such as those from CT and MRI. Eigenimage processing is based on a multivariate statistical method which identifies patterns of variance within a training set of images. Specifically it involves the application of a statistical technique called principal components analysis to a training set. For this research, the training set was a collection of 77 normal radiographs. This processing produced a set of basis images, known as eigenimages, that best describe the variance within the training set of normal images. For chest radiographs the basis images may also be referred to as 'eigenchests'. Images to be tested were described in terms of eigenimages. This identified patterns of variance likely to be normal. A new image, referred to as the remainder image, was derived by removing patterns of normal variance, thus making abnormal patterns of variance more conspicuous. The remainder image could either be presented to clinicians or used as part of a computer aided diagnosis system. For the image sets used, the discriminatory power of a classification scheme approached 90%. While the processing of the training set required significant computation time, each test image to be classified or enhanced required only a few seconds to process. Thus the system could be integrated into a clinical radiology department.

No description available.

Durante o processo de usinagem, o conhecimento da temperatura é um dos fatores mais importante na análise do estado da ferramenta. Permite o controle dos fatores mais importantes que influenciam, no uso, na vida e no desgaste da ferramenta. A temperatura na região de contato entre a peça e a ferramenta é resultante do processo de remoção de material durante a operação de corte e é difícil de se obter uma vez que, ou a peça, ou a ferramenta estão em movimento. Uma maneira de se medir a temperatura nessa situação é detectando a radiação de infravermelho. Este trabalho tem objetivo de apresentar uma nova metodologia de diagnóstico e monitoramento de operações de usinagem com o uso de imagens de infravermelho. A imagem de infravermelho fornece um mapa em tons de cinza da temperatura dos elementos participantes do processo: ferramenta, peça e cavaco. Cada tom de cinza na imagem corresponde a uma temperatura para cada material. A correspondência entre tons de cinza e a temperatura é dada pela prévia calibração da câmera de infravermelho para os materiais participantes do processo. O sistema desenvolvido neste trabalho usa uma câmera de infravermelho, uma frame grabber e um software composto por 3 módulos: o primeiro módulo faz a aquisição da imagem de infravermelho e o processamento; o segundo módulo faz a extração e o cálculo do vetor de características das imagens. Finalmente o terceiro módulo usa um algoritmo fuzzy e fornece como saída o diagnóstico do estado da ferramenta. / During machining process the temperature knowledge is one of the most important factors in tool analysis. It allows to control main factors that influence tool use, life time and wear. The temperature in the contact area between the work piece and the tool is resulting from the material remova! in cutting operation and it is too difficult to be obtained because the tool, or the work piece is in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements temperature in the process: tool, work piece and chips. Each gray tone corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is goven by previous infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed by three modules. The first module provides the image acquisition and processing. The second one does the image feature extraction and calculates the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and to supply the tool state diagnostic as output.