Cancer Recognition in Medical Image Processing using Watershed Algorithm

Lung tumor is by all accounts the basic reason for death among individuals all through the world. Survival from lung tumor is straightforwardly identified with its development at its discovery time. The prior the identification is, the higher the odds of fruitful treatment.. To upgrade malignancy location the radiologists, utilizes CT check pictures for reviewing the insides of the body.Image handling methods give a decent quality apparatus to enhancing the manual examination. Henceforth, a lung malignancy recognition framework utilizing picture handling is utilized to arrange the present of lung disease in a CT-pictures. A programmed growth discovery framework is proposed to recognize malignant tumor from the CT check pictures. The tumor discovery conspire comprises of four phases. They are preprocessing, division, include extraction and characterization. These four levels are utilized as a part of picture handling to upgrade the tumor recognizable proof exactness. The ultimate result of this paper is to discover malignancy identification.

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A Novel for Construct Knowledge Structure using Marker Watershed Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i3337.0789s319 ◽

2019 ◽

Vol 8 (9S3) ◽

pp. 1609-1612

Keyword(s):

Lung Tumor ◽

Knowledge Structure ◽

Watershed Algorithm ◽

Lung Malignancy ◽

The World ◽

Ultimate Result ◽

Discovery Time ◽

Manual Examination ◽

Four Levels ◽

Handling Methods

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A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183863 ◽

2018 ◽

pp. 194-200

Author(s):

J. Magelin Mary ◽

Chitra K. ◽

Y. Arockia Suganthi

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Processing Technique ◽

Input Image ◽

Image Processing Technique ◽

Computational Time ◽

Image Mining ◽

The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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Medical Image Processing To Detect Brain Cancer

10.34279/0923-007-001-009 ◽

2015 ◽

pp. 31

Author(s):

اسراء أكرم فاضل

Keyword(s):

Image Processing ◽

Brain Cancer ◽

Medical Image ◽

Medical Image Processing

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An Effectual Sentiment Analysis for High Classification Rates Using Medical Image Processing

Proceedings of the 4th International Conference on Machine Learning and Soft Computing ◽

10.1145/3380688.3380714 ◽

2020 ◽

Author(s):

Gaurika Jaitly ◽

Manoj Kapil

Keyword(s):

Image Processing ◽

Sentiment Analysis ◽

Medical Image ◽

Medical Image Processing

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Medical image processing with contextual style transfer

Human-centric Computing and Information Sciences ◽

10.1186/s13673-020-00251-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yin Xu ◽

Yan Li ◽

Byeong-Seok Shin

Keyword(s):

Medical Image ◽

Medical Image Processing ◽

Image Synthesis ◽

Industrial Applications ◽

Generative Models ◽

Context Aware ◽

Training Set ◽

Style Transfer ◽

Segmentation Accuracy ◽

Generative Methods

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.

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The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

Journal of Imaging ◽

10.3390/jimaging7080124 ◽

2021 ◽

Vol 7 (8) ◽

pp. 124

Author(s):

Kostas Marias

Keyword(s):

Image Processing ◽

Image Analysis ◽

Medical Image ◽

Biomarker Discovery ◽

Medical Image Processing ◽

Machine Learning Techniques ◽

Imaging Biomarker ◽

Imaging Biomarkers ◽

Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

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