3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

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