Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

This paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation

Land use / land cover change detection: an object oriented approach, Münster, Germany

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesLand use / land cover (LULC) change detection based on remote sensing (RS) data has been established as an indispensible tool for providing suitable and wide-ranging information to various decision support systems for natural resource management and sustainable development. LULC change is one of the major influencing factors for landscape changes. There are many change detection techniques developed over decades, in practice, it is still difficult to develop a suitable change detection method especially in case of urban and urban fringe areas where several impacts of complex factors are found including rapid changes from rural land uses to residential, commercial, industrial and recreational uses. Although these changes can be monitored using several techniques of RS application, adopting a suitable technique to represent the changes accurately is a challenging task. There are a number of challenges in RS application for analysis of LULC change detection. This study applies objectoriented (OO) method for mapping LULC and performing change detection analysis using post-classification technique.(...

Delineation of Surface Water Features Using RADARSAT-2 Imagery and a TOPAZ Masking Approach over the Prairie Pothole Region in Canada

The Prairie Pothole Region (PPR) is one of the most rapidly changing environments in the world. In the PPR of North America, topographic depressions are common, and they are an essential water storage element in the regional hydrological system. The accurate delineation of surface water bodies is important for a variety of reasons, including conservation, environmental management, and better understanding of hydrological and climate modeling. There are numerous surface water bodies across the northern Prairie Region, making it challenging to provide near-real-time monitoring and in situ measurements of the spatial and temporal variation in the surface water area. Satellite remote sensing is the only practical approach to delineating the surface water area of Prairie potholes on an ongoing and cost-effective basis. Optical satellite imagery is able to detect surface water but only under cloud-free conditions, a substantial limitation for operational monitoring of surface water variability. However, as an active sensor, RADARSAT-2 (RS-2) has the ability to provide data for surface water detection that can overcome the limitation of optical sensors. In this research, a threshold-based procedure was developed using Fine Wide (F0W3), Wide (W2) and Standard (S3) modes to delineate the extent of surface water areas in the St. Denis and Smith Creek study basins, Saskatchewan, Canada. RS-2 thresholding results yielded a higher number of apparent water surfaces than were visible in high-resolution optical imagery (SPOT) of comparable resolution acquired at nearly the same time. TOPAZ software was used to determine the maximum possible extent of water ponding on the surface by analyzing high-resolution LiDAR-based DEM data. Removing water bodies outside the depressions mapped by TOPAZ improved the resulting images, which corresponded more closely to the SPOT surface water images. The results demonstrate the potential of TOPAZ masking for RS-2 surface water mapping used for operational purposes

Endoscopic image analysis of aberrant crypt foci

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Furthermore, we combine the reverse diffusion procedure to further optimize the shallow output and generate the most realistic image. The proposed method is compared with ten state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability: https://github.com/longbai1006/LLCap

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%

The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning

The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is complete on 1:250,000 scale enlargements of ERTS imagery and 1:120,000 hi-flight photography. Maps of geology, soils, vegetation-land use and land resources units were interpreted to show limitations, suitabilities, and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS imagery has shown a number of features not previously mapped in Oregon. A multistage timber inventory of Ochoco National Forest was made, using ERTS images as the first stage. Inventory of forest clear-cutting practices was successfully demonstrated with color composites. Soil tonal differences in fallow fields correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic material classes was successful in separating most major classes around Newberry Caldera, Mt. Washington, and Big Summit Prairie