Enhanced algorithms for lesion detection and recognition in ultrasound breast images

Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance. [Continues.

Development Of Techniques For The Detection Of Tumours In Breast Magnetic Resonance Imaging

Kanser payudara ialah penyebab utama kematian di kalangan pesakit kanser yang melanda wanita dan kanser kedua paling lazim di seluruh dunia. Pengimejan Resonans Magnetik (MRI) adalah salah satu daripada alat-alat radiologi yang paling berkesan untuk menyaring kanser payudara. Bagaimanapun, teknik-teknik pemprosesan imej diperlukan bagi membantu pakar radiologi dalam mentafsir imej dan memisahkan wilayah tumor bagi mengurangkan jumlah positif yang palsu. Dalam kajian ini, pendekatan segmentasi dengan ciri-ciri automatik dibangunkan untuk tumor MRI payudara. Kaedah bermula dengan pemerolehan data diikuti oleh proses prapemprosesan. Ini diikuti dengan proses pengecualian garis kulit payudara menggunakan kaedah bersepadu Level Set Active Contour and Morphological Thinning. Berikutnya, kesan penting dikesan menggunakan kaedah Mean Maximum Raw Thresholding (MMRT) dicadangkan. Kemudian, pada fasa segmentasi tumor, dua kaedah diubahsuai Seeded Region Growing (SRG) dicadangkan; iaitu Breast MRI Tumour menggunakan Modified Automatic SRG (BMRI-MASRG) dan Breast MRI Tumour menggunakan SRG berdasarkan Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). Data set MRI payudara RIDER digunakan untuk penilaian dan keputusan dibandingkan dengan data set sebenar (ground truth). Daripada analisis keputusan, dapat diperhatikan bahawa pendekatan yang dicadangkan mencatat hasil-hasil hasilan yang tinggi menerusi pelbagai langkah. Keputusan pengecualian garis kulit mencatat purata prestasi yang tinggi bagi kedua-dua peringkat peringkat segmentasi sempadan (kepekaan = 0.81 dan ketentuan = 0.94 dan peringkat penyingkiran kawasan kulit (kepekaan = 0.86 dan ketentuan = 0.97). Penilaian kualiti MMRT menunjuk keputusan lebih jitu dengan purata PSNR = 69.97 dan MSE = 0.01. Dalam fasa segmentasi tumor, keputusan-keputusan kepekaan untuk dua kaedah yang dicadangkan; BMRI-MASRG dan BMRI-SRGPSOC, menunjukkan hasil segmentasi yang lebih tepat dengan purata masing-masingnya 0.82 dan 0.84. Begitu juga, hasil ketentuan mencatat prestasi lebih baik berbanding dengan cara sebelumnya. Purata BMRI-MASRG dan BMRI-SRGPSOC adalah masing-masingnya 0.90 dan 0.91. ________________________________________________________________________________________________________________________ Breast cancer is the leading cause of death amongst cancer patients afflicting women and the second most common cancer around the world. Magnetic Resonance Imaging (MRI) is one of the most effective radiology tools to screen breast cancer. However, image processing techniques are needed to help radiologists in interpreting the images and segmenting tumours regions to reduce the number of false-positive. In this study, a segmentation approach with automatic features is developed for breast MRI tumours. The methodology starts with data acquisition followed by pre-processing. This is then followed with breast skin-line exclusion using integrated method of Level Set Active Contour and Morphological Thinning. Next, regions of interests are detected using proposed Mean Maximum Raw Thresholding method (MMRT). In the tumour segmentation phase, two modified Seeded Region Growing (SRG) methods are proposed; i.e. Breast MRI Tumour using Modified Automatic SRG (BMRI-MASRG) and Breast MRI Tumour using SRG based on Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). The RIDER breast MRI dataset was used for evaluation and the results are compared with the ground truth of the dataset. From analysing the evaluation results, it can be noticed that the proposed approaches scored high results using various measures comparing to previous methods. The results of skin-line exclusion scored high average performance in both stages; border segmentation stage (sensitivity = 0.81 and specificity = 0.94) and removal stage (sensitivity = 0.86 and specificity = 0.97). The quality evaluation of MMRT showed improved results with average of PSNR = 69.97 and MSE = 0.01. In the tumour segmentation phase, the sensitivity results of the two proposed methods; BMRI-MASRG and BMRI-SRGPSOC showed more accurate segmentation with averages of 0.82 and 0.84 respectively. Similarly, the specificity results also scored better performance compared to previous methods. The averages of BMRI-MASRG and BMRI-SRGPSOC are 0.90 and 0.91 respectively

Development of advanced 3D medical analysis tools for clinical training, diagnosis and treatment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The objective of this PhD research was the development of novel 3D interactive medical platforms for medical image analysis, simulation and visualisation, with a focus on oncology images to support clinicians in managing the increasing amount of data provided by several medical image modalities. DoctorEye and Automatic Tumour Detector platforms were developed through constant interaction and feedback from expert clinicians, integrating a number of innovations in algorithms and methods, concerning image handling, segmentation, annotation, visualisation and plug-in technologies. DoctorEye is already being used in a related tumour modelling EC project (ContraCancrum) and offers several robust algorithms and tools for fast annotation, 3D visualisation and measurements to assist the clinician in better understanding the pathology of the brain area and define the treatment. It is free to use upon request and offers a user friendly environment for clinicians as it simplifies the implementation of complex algorithms and methods. It integrates a sophisticated, simple-to-use plug-in technology allowing researchers to add algorithms and methods (e.g. tumour growth and simulation algorithms for improving therapy planning) and interactively check the results. Apart from diagnostic and research purposes, it supports clinical training as it allows an expert clinician to evaluate a clinical delineation by different clinical users. The Automatic Tumour Detector focuses on abdominal images, which are more complex than those of the brain. It supports full automatic 3D detection of kidney pathology in real-time as well as 3D advanced visualisation and measurements. This is achieved through an innovative method implementing Templates. They contain rules and parameters for the Automatic Recognition Framework defined interactively by engineers based on clinicians’ 3D Golden Standard models. The Templates enable the automatic detection of kidneys and their possible abnormalities (tumours, stones and cysts). The system also supports the transmission of these Templates to another expert for a second opinion. Future versions of the proposed platforms could integrate even more sophisticated algorithms and tools and offer fully computer-aided identification of a variety of other organs and their dysfunctions

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality

IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION

Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good hard-tissue contrast. In this context, Positron Emission Tomography (PET) is a non-invasive imaging technique which has the advantage, over morphological imaging techniques, of providing functional information about the patient’s disease. In the last few years, several criteria to assess therapy response in oncological patients have been proposed, ranging from anatomical to functional assessments. Changes in tumour size are not necessarily correlated with changes in tumour viability and outcome. In addition, morphological changes resulting from therapy occur slower than functional changes. Inclusion of PET images in radiotherapy protocols is desirable because it is predictive of treatment response and provides crucial information to accurately target the oncological lesion and to escalate the radiation dose without increasing normal tissue injury. For this reason, PET may be used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the nature of PET images (low spatial resolution, high noise and weak boundary), metabolic image processing is a critical task. The aim of this Ph.D thesis is to develope smart methodologies applied to the medical imaging field to analyse different kind of problematic related to medical images and data analysis, working closely to radiologist physicians. Various issues in clinical environment have been addressed and a certain amount of improvements has been produced in various fields, such as organs and tissues segmentation and classification to delineate tumors volume using meshing learning techniques to support medical decision. In particular, the following topics have been object of this study: • Technique for Crohn’s Disease Classification using Kernel Support Vector Machine Based; • Automatic Multi-Seed Detection For MR Breast Image Segmentation; • Tissue Classification in PET Oncological Studies; • KSVM-Based System for the Definition, Validation and Identification of the Incisinal Hernia Reccurence Risk Factors; • A smart and operator independent system to delineate tumours in Positron Emission Tomography scans; 3 • Active Contour Algorithm with Discriminant Analysis for Delineating Tumors in Positron Emission Tomography; • K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor Volumes; • Tissue Classification to Support Local Active Delineation of Brain Tumors; • A fully automatic system of Positron Emission Tomography Study segmentation. This work has been developed in collaboration with the medical staff and colleagues at the: • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMED), University of Palermo • Cannizzaro Hospital of Catania • Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale delle Ricerche (CNR) of Cefalù • School of Electrical and Computer Engineering at Georgia Institute of Technology The proposed contributions have produced scientific publications in indexed computer science and medical journals and conferences. They are very useful in terms of PET and MRI image segmentation and may be used daily as a Medical Decision Support Systems to enhance the current methodology performed by healthcare operators in radiotherapy treatments. The future developments of this research concern the integration of data acquired by image analysis with the managing and processing of big data coming from a wide kind of heterogeneous sources

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

A 3D US Guidance System for Permanent Breast Seed Implantation: Development and Validation

Permanent breast seed implantation (PBSI) is a promising breast radiotherapy technique that suffers from operator dependence. We propose and have developed an intraoperative 3D ultrasound (US) guidance system for PBSI. A tracking arm mounted to a 3D US scanner registers a needle template to the image. Images were validated for linear and volumetric accuracy, and image quality in a volunteer. The tracking arm was calibrated, and the 3D image registered to the scanner. Tracked and imaged needle positions were compared to assess accuracy and a patient-specific phantom procedure guided with the system. Median/mean linear and volumetric error was ±1.1% and ±4.1%, respectively, with clinically suitable volunteer scans. Mean tracking arm error was 0.43mm and 3D US target registration error ≤0.87mm. Mean needle tip/trajectory error was 2.46mm/1.55°. Modelled mean phantom procedure seed displacement was 2.50mm. To our knowledge, this is the first reported PBSI phantom procedure with intraoperative 3D image guidance

Intelligent Medical Image Segmentation Using Evolving Fuzzy Sets

Image segmentation is an important step in the image analysis process. Current image segmentation techniques, however, require that the user tune several parameters in order to obtain maximum segmentation accuracy, a computationally inefficient approach, especially when a large number of images must be processed sequentially in real time. Another major challenge, particularly with medical image analysis, is the discrepancy between objective measures for assessing and guiding the segmentation process, on the one hand, and the subjective perception of the end users (e.g., clinicians), on the other. Hence, the setting and adjustment of parameters for medical image segmentation should be performed in a manner that incorporates user feedback. Despite the substantial number of techniques proposed in recent years, accurate segmentation of digital images remains a challenging task for automated computer algorithms. Approaches based on machine learning hold particular promise in this regard because, in many applications, including medical image analysis, frequent user intervention can be assumed as a means of correcting the results, thereby generating valuable feedback for algorithmic learning. This thesis presents an investigation of the use of evolving fuzzy systems for designing a method that overcomes the problems associated with medical image segmentation. An evolving fuzzy system can be trained using a set of invariant features, along with their optimum parameters, which act as a target for the system. Evolving fuzzy systems are also capable of adjusting parameters based on online updates of their rule base. This thesis proposes three different approaches that employ an evolving fuzzy system for the continual adjustment of the parameters of any medical image segmentation technique. The first proposed approach is based on evolving fuzzy image segmentation (EFIS). EFIS can adjust the parameters of existing segmentation methods and switch between them or fuse their results. The evolving rules have been applied for breast ultrasound images, with EFIS being used to adjust the parameters of three segmentation methods: global thresholding, region growing, and statistical region merging. The results for ten independent experiments for each of the three methods show average increases in accuracy of 5\%, 12\% and 9\% respectively. A comparison of the EFIS results with those obtained using five other thresholding methods revealed improvements. On the other hand, EFIS has some weak points, such as some fixed parameters and an inefficient feature calculation process. The second approach proposed as a means of overcoming the problems with EFIS is a new version of EFIS, called self-configuring EFIS (SC-EFIS). SC-EFIS uses the available data to estimate all of the parameters that are fixed in EFIS and has a feature selection process that selects suitable features based on current data. SC-EFIS was evaluated using the same three methods as for EFIS. The results show that SC-EFIS is competitive with EFIS but provides a higher level of automation. In the third approach, SC-EFIS is used to dynamically adjust more than one parameter, for example, three parameters of the normalized cut (N-cut) segmentation technique. This method, called multi-parametric SC-EFIS (MSC-EFIS), was applied to magnetic resonance images (MRIs) of the bladder and to breast ultrasound images. The results show the ability of MSC-EFIS to adjust multiple parameters. For ten independent experiments for each of the bladder and the breast images, this approach produced average accuracies that are 8\% and 16\% higher respectively, compared with their default values. The experimental results indicate that the proposed algorithms show significant promise in enhancing image segmentation, especially for medical applications