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Paddy is the most important crop in Asian country. Most of the people depend on rice for their food, so rice is considered as staple food in Asian country. Rice plant is affected by many diseases that affect the farmers in yield loss. In this paper, proposed a method for identification of Blast and Brown Spot diseases. Global threshold method has been applied and kNN classifier has been used to classify...
The infection in shrimp is a significant issue which makes decline in production. In this manner, the investigation of premium is the region on shrimp infection happens. By utilizing proficient computer programming software to consequently distinguish the shrimp infection by separating features. Image processing systems are utilized for location and acknowledgment of illnesses in different zones which...
In the world today there are number of skin diseases which are found in humans, animals and plants. The illness caused by bacteria or infections will be known as skin disease like yeast infection, allergy, eczema, brown spot. These skin diseases have dangerous effect on skin and they keep on spreading over time. To control them from spreading it is necessary to identify these diseases at their starting...
Citrus is nutrition fruit and required for human being. This is one for major cash crop in India. The various bacterial and microorganisms attack on plant affects various parts like stem, leaves and fruit. This research explain about implementation of image processing to determiner abnormalities and diseases over citrus leaves. This research model divided into four parts. First stage is image pre-processing...
Indian economy highly depends on agricultural productivity. An important role is played by the detection of disease to obtain a perfect results in agriculture, and it is natural to have disease in plants. Proper care should be taken in this area for product quality and quantity. To reduce the large amount of monitoring in field automatic detection techniques can be used. This paper discuss different...
Modern phenotyping and plant disease detection provide promising step towards food security and sustainable agriculture. In particular, imaging and computer vision based phenotyping offers the ability to study quantitative plant physiology. On the contrary, manual interpretation requires tremendous amount of work, expertise in plant diseases, and also requires excessive processing time. In this work,...
In this study, the unsupervised clustering method namely K-means algorithm is applied for identifying the multiple sclerosis (MS) lesions in magnetic resonance (MR) images automatically. MS lesion detection is essential for diagnosing the disease and monitoring its progression. The automated method aims to eliminate user-dependent classification errors and to improve computational capacity in detecting...
We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines...
Automatic identification of side branch and main vascular measurements in IVOCT images take critical roles in pre-interventional decision making for coronary artery disease treatment. Very little works have been presented on these tasks. In this paper, we proposed a novel side branch identification algorithm which utilizes a newly defined global curvature feature to identify the ostium of side branch...
Multi-atlas based label fusionmethods have been successfully used for medical image segmentation. In the field of brain region segmentation, multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases. Most of existing multi-atlas based methods usually use intensity feature, which is hard to capture high-order information...
Based on the fact that coronary heart disease (CHD) is the leading cause of death among all cardiovascular abnormalities, clinicians are keen in early detection and continuous monitoring of arterial atherosclerosis. Conventional cardiac catheterization method yields 2D angiograms of cardiac vasculature with possibly missed abnormalities as well as its invasive nature involves risk to the patient....
Kidney disease is one of the life threatening diseases prevailing among the humans. Most of the people die because of kidney diseases. It occurs due to the change which is occurring in the production of DNA cells (cancer), protein deficiency (nephritis) etc., In this paper, an automatic detection of the kidney diseases from CT abdominal images is proposed. First, the CT abdominal images are acquired...
Clustering is an unsupervised technique is used for organizing the data for efficient retrieval. This is mainly used in pattern reorganization and data analysis. Today many cluster analysis techniques are used for data analysis and have proven to be very useful in segmentation. Performance of these algorithms is data dependent. In this paper K-Means and Fuzzy C-Means are implemented for segmenting...
This paper presents the development of an automatic system for the classification of tooth wear disease diagnosis. Abnormal detection, disease detection and classification of oral images are substantial in the clinical research. Automatic diagnosis of the oral disease helps the medical practitioner to make decisions easily about the diagnosis process. The diagnosis models can be analyzed with the...
Malaria Is a serious health Issue and causes a million deaths in a year globally. The present gold standard of malaria diagnosis, recommended by world health organization (WHO) is the manual microscopy method of Giemsa-stained blood smears, which is a laborious process requiring expert technicians. This paper presents a robust and fast algorithm that identifies Malaria parasites from both thin and...
Agriculture is the foundation of our country. India is an agrarian nation where the majority of the populaces rely upon agribusiness. Investigation in farming is pointed towards expanding efficiency and profit. There are several automated systems already available which are developed for irrigation control and environmental monitoring in the field. Hence this project aims to monitor the plant growth...
Vascular diseases are the important sources of morbidity and mortality throughout the world. Computer-assisted Detection(CAD) and segmentation of blood vessels in MR angiography are essential and crucial for medical computing tools for clinical assessment of vascular diseases. Segmentation is a process of partitioning an angiogram into non-overlapping background and vascular regions. This article...
This paper presents an automatic method for detecting lumen and media-adventitia boundaries from “Intravascular Ultrasound Image” (IVUS). The linear stretching equation is proposed to enhance the contrast of IVUS image. A complexity reduction technique for IVUS image is conducted by an adaptive k-mean clustering. Finally, conventional integrated processes based on mathematics morphology and convex...
The corpus callosum (CC) is a set of neural fibers in the cerebral cortex, responsible for facilitating inter-hemispheric communication. The CC structural characteristics appear as an essential element for studying healthy subjects and patients diagnosed with neurodegenerative diseases. Due to its size, the CC is usually divided into smaller regions, also known as parcellation. Since there are no...
Agrarian production is that trait on which our nation's economy immensely depends. This is the motivation that recognition of leaves unhealthiness is the solution for saving the reduction of crops and productivity. It requisite enormous amount of work, mastery in the leaf diseases, and additionally need the extreme amount of time. Thus, image processing techniques are applied for the discovering and...
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