Digital Intelligence

Digital Intelligence

Image Processing or Digital Image Processing is technique to improve image quality by applying mathematical operations. Image Processing Projects involves modifying images by identification of its two-dimensional signal and enhancing it by comparing with standard signal. The second technique of image processing project is to modify characteristic parameters related to digital images.

Applications of Digital Image Processing

The field of digital image processing has experienced continuous and significant expansion in recent years. The usefulness of this technology is apparent in many different disciplines covering medicine through remote sensing. The advances and wide availability of image processing hardware has further enhanced the usefulness of image processing. The Application of Digital Image Processing welcomes contributions of new results and novel techniques from this important technology. The broad areas of digital image processing applications, include medical applications, restorations and enhancements , digital cinema, image transmission and coding, color processing ,remote sensing, robot vision, hybrid techniques, facsimile, pattern recognition, registration techniques, multidimensional image processing image processing architectures and workstations, video processing ,programmable DSPs for video coding, high-resolution display, high-quality color representation, super-high-definition image processing, impact of standardization on image processing.

Lung cancer detection using digital Image processing On CT scan Images

Lung cancer main disease cause of death of among throughout the world. Lung cancer is causing very high mortality rate. There are various cancer tumours such as lung cancer, breast Cancer, etc. Early stage detection of lung cancer is important for successful treatment. Diagnosis is based on Computed Tomography (CT) images. In this Histogram Equalization used to pre-processing of the images and feature extraction process and classifier to check the condition of a patient in its early stage whether it is normal or abnormal.

Quality Improvement in Kidney Stone Detection on Computed Tomography Images

Kidney-Urine-Belly computed tomography (KUB CT) analysis is an imaging modality that has the potential to enhance kidney stone screening and diagnosis. This study explored the development of a semi-automated program that used image processing techniques and geometry principles to define the boundary, and segmentation of the kidney area, and to enhance kidney stone detection. It marked detected kidney stones and provided an output that identifies the size and location of the kidney based on pixel count. The program was tested on standard KUB CT scan slides from 39 patients at Imam Reza Hospital in Iran who were divided into two groups based on the presence and absence of kidney stones in their hospital records. Of these, the program generated six inconsistent results which were attributed to the poor quality of the original CT scans. Results showed that the program has 84.61 per cent accuracy, which suggests the program’s potential in diagnostic efficiency for kidney stone detection.

Brain tumour segmentation based on a hybrid clustering technique

Image segmentation refers to the process of partitioning an image into mutually exclusive regions. It can be considered as the most essential and crucial process for facilitating the delineation, characterization, and visualization of regions of interest in any medical image. Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms and techniques available for image segmentation but still there needs to develop an efficient, fast technique of medical image segmentation. This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumour detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state-of-the-art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.


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