Owing to imaging equipment's environment and limitations, the images obtained in industrial cyber-physical systems (ICPSs) are degraded and available in various visual appearances. The process of highlighting the hidden contents in night-time contrast-distorted images is complex. Earlier approaches have solved this problem from a different perspective and achieved remarkable results that are generally unsatisfactory for images with diverse illumination distortions in ICPSs. Hence, an effective visibility enhancement model is proposed to eliminate inconsistent color casts while highlighting more hidden content for improved inspection, safety in large spaces, and monitoring of large systems. Our proposed model has four steps: 1) removal of the unnatural color cast via a white balance technique, 2) use of probability density and softplus functions to process the images with the actual color cast, 3) using an optimization algorithm to estimate the illumination and adjusting it using a nonlinear function, and 4) blending by multiscale fusion to obtain the most effective visual result. Evaluation of ten benchmark datasets using 14 quality metrics for 22 conventional and modern algorithms shows that our approach is robust, flexible, and applicable to numerous vision-based applications, such as ICPSs, autonomous vehicles, smart cameras, smart mobility, and transportation, especially in low-light environments. 

Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems

The biased X-ray field and the subtle difference in X-ray attenuation between normal and abnormal breast tissues prevent the biomedical sensors to generate mammograms with good quality. These are the common imperfections in the acquisition systems (sensor) of Mammography which contribute towards the degradation of mammogram image quality. This paper aims to develop a contrast enhancement model for mammograms so that the human or machine vision can distinguish easily among the variants of breast lesions and predict their severity. The proposed mammogram enhancement model exploits locally the proprieties of the sigmoidal function based on multi-objective Genetic Algorithm for improving the contrast of lesions along with a simultaneous increment in various Image Quality Assessment (IQA) parameters like: $\textit {EME}$ , $\textit {EC}$ , $\textit {AMBEn}$ and $\textit {FSIM}$ . Herein, the multi-objective optimization problem is transformed into a single-objective optimization to find a unique solution for mammogram enhancement. An increase in these IQA metrics is indicative of better contrast, edge content, and conservation of brightness and other diagnostics information. Simulations of the proposed enhancement model are carried out on mammograms from the mini-MIAS database for benchmarking and validation of results.

A Contrast Enhancement Model for X-Ray Mammograms Using Modified Local s-Curve Transformation Based on Multi-Objective Optimization

Breast cancer has increased decidedly among women. But with early diagnosis, a positive response to treatment can be given. Researchers are conducting various studies in imaging methods to detect the disease early and accurately. In this study, 9 cancerous images taken from the TCİA image data bank were detected by K-mean clustering and the Otsu threshold method. Performance metrics were evaluated by comparing them with marked reference images (ground truth) by the radiologist. For the clustering process, TPR (True Positive Rate) 0.89, FPR (False Positive Rate) 0.14, similarity 0.67, accuracy 0.87, sensitivity 0.89, exact hit ratio 0.86, specificity 0.87, F Score 0.87 were found, respectively. For Otsu, TPR (True Positive Rate) 0.84, FPR (False Positive Rate) 0.12, similarity 0.73, accuracy 0.84, sensitivity 0.84, exact hit 0.86, specificity 0.87, F Score 0.84 were calculated. The aim of this study is to determine the tumor boundaries more accurately and to use them in imaging devices in the field of health with pixel-based segmentation. As a result, both methods were successful can be used in the field and close to each other. 

/pdf/diagnosis-of-breast-cancer-by-k-mean-clustering-and-otsu-nevyli9t.pdf

Diagnosis of Breast Cancer by K-Mean Clustering and Otsu Thresholding Segmentation Methods

breast cancer is the most spread cancer globally. The disease symptoms and treatments differ from one patient to another. Early detection can considerably alter the outcome for breast cancer patients. CAD systems playa fundamental role in the prognosis of the disease. In this paper, a proposed ensemble model based on the stacking technique uses logistic regression LR as a Meta-model and four machine learning algorithms Decision Tree DT, Random Forest RF, extreme gradient boosting XGboost, and adaptive boosting Adaboost as base models. The data used in this study is the breast cancer Wisconsin dataset. After preprocessing and features selection, the models were trained and then tested. Evaluation metrics were calculated to evaluate the system's global performance. Results indicate that the stacking techniques can enhance the performance of the system. The ensemble model outperformed the base models with an accuracy of over 98% and a precision of 100% which makes it of service for radiologists in breast tumor classification tasks. 

A CAD System Based On a Stacked Ensemble Model and ML Techniques for Breast Cancer Prognosis

Jalur puntara jawa merupakan jalan raya wilayah utara jawa membentang dari merak sampai ke banyuwangi. Jalur pantura ini merupakan jalur padat yang dilalui oleh kendaraan baik roda 4 maupun lebih. Beban jalan pantura per tahun mencapai 1 juta ton. Selain itu juga dengan beban ini merupakan penyumbang penggunaan BBM (bahan bakar minyak) yang lebih sebesar 42 ribu kiloliter dan mengakibatkan pemberi emisi udara cukup banyak sebesar 350 ton per hari.  Dengan demikian semakin banyak kendaraan yang melintas jalur pantura, sehingga perlu diketahui tentang jumlah kendaraan yang melintas dijalur pantura secara otomatis oleh sistem. Data ini dapat dijadikan rujukan beban jalan pantura secara tepat. Pada penelitian ini, menggunakan beberapa algoritma dari blob detection dengan menggunakan library openCV dan kalman filter. Hasil dari deteksi dengan menggunakan bolb deteksi masih terdapat beberapa error yang cukup besar dan menggunakan kalman filter didapatkan 78.81 %.

Aplikasi Penghitung Kendaraan Pada Jalur Pantura Menggunakan Blob Deteksi Dan Kalman Filter

Mammogram image enhancement plays an important role in the accuracy of the diagnosis. Resulting images must allow a good contrast and better appearance of the limiting edges between different areas in the image, while preserving both information and brightness. In this paper, an adaptive local gray-level s-curve transformation is proposed. The main aim is to improve as much contrast of mammogram images as possible. The principle is to find all the parameters of the local gray-level transformation for each image which will allow for a better improvement using the genetic algorithm that is among global optimization methods. The evaluation of the results found is done based on image quality assessment (IQA) metrics and visual inspection in comparison with three existing techniques based on histogram equalization.

Adaptive Local Gray-Level Transformation Based on Variable S-Curve for Contrast Enhancement of Mammogram Images

Computer vision blob detection and tracking is used for precise characterization of optical fiber sensor probe chemically etched. We demonstrate a high degree of repeatability in fabrication of polarization maintaining optical fiber evanescent sensor by tracking etching rates of fiber dopants. We achieved a resolution higher than 0.1 μm by using blob detection processing and centroid algorithm of the fiber probe microscopes images.

Computer blob detection and tracking for highly repeatable optical fiber sensor

We present a new method for controlling the cladding thickness of etched Polarization Maintaining Fiber based sensor in order to precisely control the sensitivity and the optical characteristics of the sensor. This method is based on the experimental measurement of the chemical etching rates of the fiber materials by measuring longitudinal etching rates of doped materials by perpendicular immersion of a polarization maintaining optical fibre probe under the same operating conditions and manufacturing of the sensors. Computer blob tracking are used to precise measurement of both cladding and stress parts etching rates of Panda-type polarization maintaining optical fibre. The experimental etching rates are used in an algorithm to simulate the geometry of the etched fibre sensor in real-time. A high degree of sensor sensitivity repeatability has been achieved with a resolution on the cladding thickness better than 0.4um.

/pdf/highly-repeatable-polarization-maintaining-optical-fiber-3qpcz3n8ps.pdf

Highly Repeatable Polarization Maintaining Optical Fiber Evanescent Sensor Fabrication Method

Improvements on sensitivity resolution of Polarization Maintaining optical fibre sensor based on evanescent field interaction have been achieved by image processing and tracking of transversally immersed polarization maintaining optical fibre probe on HF acid. Etching rates of fibre dopants are tracked in real time and injected in an algorithm for real time simulation of the Polarization Maintaining optical fibre sensor. Resolution sensitivity better than 0.2 dB has been achieved, allowing a high degree of repeatability of Polarization maintaining optical fibre sensors.

Improvements on Sensitivity Repeatability of Polarization Maintaining Optical Fibre Sensor Using Computer Blob Detection and Tracking

Breast cancer is the second disease causing death in women. The screening of breast cancer for early detection is an effective way to reduce mortality. Generally, mammography is the most effective imaging modality recommended by radiologists. Therefore, a high number of mammogram images have to be analyzed. Indeed, the expertise of radiologists and the quality of the images to be analyzed play a crucial role in the accuracy of the diagnosis. The use of CAD systems allow improving the accuracy of diagnosis by reducing the number of false-positive and the false-negative. In this paper, to assist radiologists to make the right decision by showing them the probably suspect area, a full automatic CADe is presented. The preprocessing step aims at delimiting the ROI by removing artifact and pectoral muscle using global thresholding method, morphological operators and seeded region growing method, followed by contrast enhancement of the ROI found in the first step using the technique named “adaptive local gray level transformation based on variable s-curve”. At last, the ROI is segmented using k-means clustering. Both contrast enhancement method and the segmentation method are performed by genetic algorithm to optimize the outcomes of each step.

Fully Automatic Computer-Aided Detection of Breast Cancer based on Genetic Algorithm Optimization

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