Intelligent System for Accurate Prediction and Detection of Alzheimer’s Disease

2019 ◽  
pp. 51-56
Author(s):  
Anusha Nambirajam K ◽  
Siva Subramanian T ◽  
Priyadharshini R
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Digital Image Processing ◽  
Digital Image ◽  
Intelligent System ◽  
Essential Feature ◽  
Memory Loss ◽  
Video Frame ◽  
K Nearest Neighbor

Image processing is a process of converting an image into digital form and achieve some maneuvers on it, in order to get an enhanced image or to mine some useful information from it. It is a type of signal dispensation in which the input is an image, like video frame or photograph and output, may be image along with its characteristics and features associated with that image. An image is defined as a two-dimensional function F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. When x, y, and amplitude values of F are finite, we call it a digital image. Image processing mainly consists of three basic steps. They are as follows: Initially, the image will be imported by using an optical scanner or by high-digital photography. Then the captured image will be subjected to the analyzation and manipulation process. These process also includes compression of data, enhancement of the image and spotting the patterns that are not visible to human eyes like satellite photography. Finally, the output will be obtained as an alternative image or any other essential feature extraction of the pre-processed image. Image Processing consists of two major types. They are  Analog Image Processing and Digital Image Processing. Digital Image Processing is a process in which a digital system is developed for processing a digital image and extracting feature form of results. Digital Image Processing works on the basis of an algorithm. An Intelligent System for Accurate Detection and Prediction of Alzheimer’s Disease mainly uses the k-nearest neighbor algorithm. Alzheimer’s  Disease is a type of disease in which the brain cells tend to die away and cause memory loss. In our proposed model we predict the accuracy of the amount of memory loss occurred in an affected brain. This system is mainly developed for helping the doctors and psychologists to obtain a maximum level of accuracy of the patient’s affected brain.

scholarly journals Maize Plant Desease Identification (Zea Mays L. Saccharata) Using Image Processing and K-Nearest Neighbor (K-Nn)

2018 ◽  
Vol 7 (3.20) ◽  
pp. 402
Author(s):  
Prihastuti Harsan ◽  
Arie Qurania ◽  
Karina Damayanti
Keyword(s):  
Image Processing ◽  
Zea Mays ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Disease ◽  
Zea Mays L ◽  
Maize Plant ◽  
Computer Algorithms ◽  
K Nearest Neighbor ◽  
Plant Pests

Plant pests of maize are known to attack in all phases of corn plant growth (Zea mays L. saccharata), both vegetative and generative. Common pests found in maize are seed flies (Atherigona sp.), Stem borers (Ostrinia furnacalis), Boricoverpa armigera, leaf-eaters (Spodoptera litura). The process of identification of maize plant disease is done through laboratory analysis and direct observation. The time required to obtain the identification result is 4 (four) months. Plant pests will attack some parts of the plant, including leaves, stems and fruit. Early detection is usually done through leaves. Plant pests will attack the plant leaf area with certain characteristics. Digital image processing is the use of computer algorithms to perform image processing on digital images. Identification of maize plant disease can apply image processing techniques through the characteristics or symptoms of disease raised on the leaves. Characteristic of attacks by pests in maize plants can be detected through the colors and patterns that appear on the leaves. This research performs implementation of digital image processing method to identify disease in maize plant caused by pest. The disease is Hawar Leaf, Bulai (Downy Midew), Hama Grasshopper, Leaf Spot (Sourthern Leaf Blight). Through color and edge detection, the accuracy obtained is 91.7%. 

scholarly journals Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaochen Tang ◽  
Yunbo An ◽  
Congshan Li
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Medical Image ◽  
Intelligent System ◽  
Recognition System ◽  
Processing Technology ◽  
Image Processing Technology ◽  
Pathological Image

With the development of digital image technology, judging diseases by medical image plays an important role in medical diagnosis. Mammography is the most effective imaging examination method for breast cancer at present. Intelligent segmentation and identification of breast cancer images and judging their size and classification by digital image processing technology can promote the development of clinical medicine. This paper introduces the preprocessing technology of breast cancer pathological image and medical image recognition technology of breast cancer. In order to improve the segmentation accuracy of image processing and optimize, the segmentation recognition ability in digital mammography was improved. Based on the technical basis of pathological image analysis of breast cancer, the architecture of intelligent segmentation and recognition system for breast cancer was constructed, and each functional module of intelligent system was introduced in detail. Based on digital image processing technology, filtering technology is used to reduce dryness and improve the clarity of the image. Public datasets INBreast and DDSM-BCRP were used to verify system’s performance, and it was tested on the breast cancer image test set. The experiment shows that the comprehensive performance of the intelligent segmentation and recognition system can realize the segmentation and recognition of breast cancer and has higher accuracy and interpretability, which is helpful to improve the diagnosis of doctors.

scholarly journals Image processing for early detection of Alzheimer’s disease using Iridology

2020 ◽  
Vol 11 (4) ◽  
pp. 5555-5559
Author(s):  
Asuntha A ◽  
Sai Kalyan Reddy R ◽  
Vamshikrishna K ◽  
Premsagar N
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Region Of Interest ◽  
Colour Pattern ◽  
Training Methods ◽  
Iris Image ◽  
Alternative Research ◽  
The Brain

Alzheimer's disease is caused by genetics, personal lifestyle and other environmental factors. It is an irreversible disease that slowly destroys the brain memory cells. There are no specific methods for the detection of Alzheimer's disease. The primary symptoms of Alzheimer's disease are memory loss, difficulty in thinking, a problem in writing and speaking and others. Iridology is alternative research that has gained more popularity in recent years, which studies the alterations of the iris in correspondence with the organs of the human body. The combination of digital image processing with Iridology gives an excellent opportunity to explore and learn about different neuronal diseases, specifically Alzheimer's disease. In this work, MATLAB software is applied to determine the colour, pattern and other factors that show the existence of Alzheimer's disease. The noise in the iris image is removed by the Gaussian filter, followed by histogram analyses and cropping. The Hough circle transform is used to identify the region of interest and to convert the circular iris image into rectangle form. In the training methods, the SVM and CNN classifiers are used to classify whether the person has Alzheimer's disease. Finally, the results are compared with the real-time images.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

Complementary Z-Contrast Imaging and Digital Image Processing

1985 ◽  
Vol 43 ◽  
pp. 304-305
Author(s):  
K. N. Colonna ◽  
G. Oliphant
Keyword(s):  
Image Processing ◽  
Heavy Metal ◽  
Digital Image Processing ◽  
Digital Image ◽  
Biological Tissue ◽  
Biological Imaging ◽  
Tissue Preparation ◽  
Contrast Imaging ◽  
Imaging Tool ◽  
Z Contrast

Harmonious use of Z-contrast imaging and digital image processing as an analytical imaging tool was developed and demonstrated in studying the elemental constitution of human and maturing rabbit spermatozoa. Due to its analog origin (Fig. 1), the Z-contrast image offers information unique to the science of biological imaging. Despite the information and distinct advantages it offers, the potential of Z-contrast imaging is extremely limited without the application of techniques of digital image processing. For the first time in biological imaging, this study demonstrates the tremendous potential involved in the complementary use of Z-contrast imaging and digital image processing.Imaging in the Z-contrast mode is powerful for three distinct reasons, the first of which involves tissue preparation. It affords biologists the opportunity to visualize biological tissue without the use of heavy metal fixatives and stains. For years biologists have used heavy metal components to compensate for the limited electron scattering properties of biological tissue.

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