Intensity Adjustment and Noise Removal for Medical Image Enhancement

Introduction: Image contrast enhancement is an image processing method in which the output image has high quality display. Medical images have prominent role in modern diagnosis; therefore, this study aimed to enhance the quality of medical images in order to help radiologists and surgeons in finding abnormal areas. Method: The methods used in this study to enhance medical images quality are categorized into two groups; intensity adjustment and noise removal. Intensity adjustment methods including techniques for mapping image intensity values to the new domain. The second group including methods to remove noise from the images. Medical images used in this study including images of spine, brain, lung and breast. Results: The results were analyzed based on five criteria including the number of detected edges, PCNR, Image Quality Index, AMBE and visual quality that the number of detected edges in images of spine, brain, lungs and breast were 6465, 10305, 16266 and 13509, respectively. Conclusion: The results show that the methods with intensity adjustment technique have better performance in criteria such as the number of detected edges and image visual assessment. However, the other method include in noise removal technique perform more effectively in PCNR, Image Quality Index and AMBE measure

A Future for Integrated Diagnostic Helping

International audienceMedical systems used for exploration or diagnostic helping impose high applicative constraints such as real time image acquisition and displaying. A large part of computing requirement of these systems is devoted to image processing. This chapter provides clues to transfer consumers computing architecture approaches to the benefit of medical applications. The goal is to obtain fully integrated devices from diagnostic helping to autonomous lab on chip while taking into account medical domain specific constraints.This expertise is structured as follows: the first part analyzes vision based medical applications in order to extract essentials processing blocks and to show the similarities between consumer’s and medical vision based applications. The second part is devoted to the determination of elementary operators which are mostly needed in both domains. Computing capacities that are required by these operators and applications are compared to the state-of-the-art architectures in order to define an efficient algorithm-architecture adequation. Finally this part demonstrates that it's possible to use highly constrained computing architectures designed for consumers handled devices in application to medical domain. This is based on the example of a high definition (HD) video processing architecture designed to be integrated into smart phone or highly embedded components. This expertise paves the way for the industrialisation of intergraded autonomous diagnostichelping devices, by showing the feasibility of such systems. Their future use would also free the medical staff from many logistical constraints due the deployment of today’s cumbersome systems

Classification of Plasmodium Malariae dan Plasmodium Ovale in Microscopic Thin Blood Smear Digital Images

Malaria is one of the global diseases, which mostly found in eastern Indonesia. It is caused by Plasmodium parasite infection, with four type of common species that are Plasmodium ovale (PO), Plasmodium Malaria (PM), Plasmodium falciparum (PF) and Plasmodium vivax (PV). Malaria can be detected by taking a microscopic analysis from a patient blood sample. Although it is a gold standard of malaria identification according to the WHO, this method has a risk of miss diagnosis due to the human factors. This study proposed a classification method with morphological features of PM and PO in order to help the medical expertise in identifying the malaria parasite from a thin blood smear digital microscopic image. The data used are digital images that have been through the Region of Interest (ROI) determination process. Furthermore, the process followed by improving the morphological and feature extraction of shapes and colors. Based on these obtained features, the parasites are classified by using the multilayer perceptron method. From this study, we found that the classification system has the accuracy of 95%, the sensitivity of 93%, and the specificity of 97%

Laparoscopic Image Recovery and Stereo Matching

Laparoscopic imaging can play a significant role in the minimally invasive surgical procedure. However, laparoscopic images often suffer from insufficient and irregular light sources, specular highlight surfaces, and a lack of depth information. These problems can negatively influence the surgeons during surgery, and lead to erroneous visual tracking and potential surgical risks. Thus, developing effective image-processing algorithms for laparoscopic vision recovery and stereo matching is of significant importance. Most related algorithms are effective on nature images, but less effective on laparoscopic images. The first purpose of this thesis is to restore low-light laparoscopic vision, where an effective image enhancement method is proposed by identifying different illumination regions and designing the enhancement criteria for desired image quality. This method can enhance the low-light region by reducing noise amplification during the enhancement process. In addition, this thesis also proposes a simplified Retinex optimization method for non-uniform illumination enhancement. By integrating the prior information of the illumination and reflectance into the optimization process, this method can significantly enhance the dark region while preserving naturalness, texture details, and image structures. Moreover, due to the replacement of the total variation term with two $l_2$-norm terms, the proposed algorithm has a significant computational advantage. Second, a global optimization method for specular highlight removal from a single laparoscopic image is proposed. This method consists of a modified dichromatic reflection model and a novel diffuse chromaticity estimation technique. Due to utilizing the limited color variation of the laparoscopic image, the estimated diffuse chromaticity can approximate the true diffuse chromaticity, which allows us to effectively remove the specular highlight with texture detail preservation. Third, a robust edge-preserving stereo matching method is proposed, based on sparse feature matching, left and right illumination equalization, and refined disparity optimization processes. The sparse feature matching and illumination equalization techniques can provide a good disparity map initialization so that our refined disparity optimization can quickly obtain an accurate disparity map. This approach is particularly promising on surgical tool edges, smooth soft tissues, and surfaces with strong specular highlight

Development, evaluation and optimization of image based methods for monitoring crystallization processes

Ph.DDOCTOR OF PHILOSOPH

On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems