An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs

Content Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval

SMART TECHNIQUES FOR FAST MEDICAL IMAGE ANALYSIS AND PROCESSING

Medical Imaging has become an important transversal applications and re- search field that embraces a great variety of sciences. Imaging is the central science of measurement in diagnosis and treating diseases. The effort of the technological progress has made possible human imaging starting from a single molecule to the whole body. The open challenge is to treat the huge amount of medical informations with the use of smart and fast techniques that allows clinical and images data analysis and processing. In this ph.D. Thesis, many issues have been addressed and a certain amount of improvement in various fields have been produced, such as biom- etry, organs and tissues segmentation, MRI thermometry, medical reports retrieval and classification. The topic prefixed at the beginning of this ph.D. route was to analyze, understand, and give a step over to various kind of problematics related to Medical Images and Data analysis, working closely to radiologist physicians, with specific equipments, and following the common denominator of fast and smart methodologies applied to the medical imaging issue. A series of contribution have been carried out in fields such as: • proposing two different kind of multimodal biometric authentication systems that investigates fingerprint and iris fusion and processing; • applying expert systems to the issue of data validation, comparing and validating data to two different methodologies that assess liver iron overload in thalassemic patients;• addressing and improving non-invasive referenceless thermometry by using Radial Basis Function as interpolator; • applying the multi-seed region growing method to the segmentation of CT liver dataset; • proposing a novel unsupervised voxel-based morphology method for MRI brain segmentation by using k-means clustering and neural net- work classification; • proposing a novel ontology-based algorithm for information retrieval from mammographic text reports. The above work has been developed with the cooperation of the medical staff of the “Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi” and the “Scuola di Specializzazione in Radiodiagnostica" of the Università degli Studi di Palermo. All the proposed contributions show good performance using the stan- dard metrics. Most of them have produced scientific publications in com- puter science venues as well as in radiological venues. In addition, some specific frameworks, such as OsiriX, have been used to improve usability and easiness of the developed systems

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p