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Image enhancement is a critical component in getting a good segmentation, especially for x-ray images. Magnification of the contrast and sharpness of the image will increase the accuracy of the subsequent modules for an autonomous disease diagnosis system. In this paper, we analyze various methods of preprocessing techniques for vertebral bone segmentation. Three methods are considered which are histogram equalization (HE), gamma correction (GC) and contrast limited adaptive histogram equalizer (CLAHE). This work aims to compare and quantify the precision and accuracy of the techniques that are used to enhance the image quality. Experimental results of the system yield favorable results where the most accurate technique is CLAHE, followed by GC and HE.

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Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.

Abstract:Automatic inspection based on a real-time machine vision system may serve as substitute for the manual human visual inspection of flux defects in Printed Circuit Boards (PCBs), which often cause damage on the board in the form of corrosions that harm the assembly. The concept of automatic inspection contributes to the improvement of the manufacturing quality of PCBs and facilitates their approval or rejection. The Automatic Inspection System for Printed Circuit Boards (AIS-PCB) is developed with the capability to identify the defects and the quality of PCBs. It is based on a real time system machine vision. The developed AIS-PCB is capable of detecting, indexing and classifying by measuring the flux defects in PCBs during the re-flow of the real-time process. The AIS-PCB is The total automation control system is the core of the AIS-PCB. This system consist of vision inspection station, mechanical loader and unloader, final decision station and the pneumatic system handler. To detect and classify the quality of PCBs, segmentation in conjunction with Radon transform approaches are used for feature indexing and line detection based on the gradient field of PCB images. The Feed-Forward Back-Propagation (FFBP) model is used to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed to learn and match the targets. The images of each PCB classes are used as inputs to the classification module. The obtained results from the classification and rule decision are used to establish the receiver operating characteristic curve. The classifier, which is based on the proposed approach and is tested on the PCBs from a factory's production line, achieves a sorting Coefficient Of Efficiency (COE >95%). The developed AIS-PCB system shows promising results in successfully segmenting and classifying flux defects in PCBs through computerized visual information and facilitates their automatic inspection, thereby aiding humans in conducting rapid inspections.

BackgroundContent-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.MethodsIn this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures.ResultsExperimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets.ConclusionsThe proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.