Filtering of Mammograms Based on Convolution with Directional Fractal Masks to Enhance Microcalcifications

The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature.

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Detection of Leukemia Using Image Processing

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i4.632 ◽

2018 ◽

Vol 8 (4) ◽

pp. 61

Author(s):

A. Sivasangari ◽

G. Sasikumar

Keyword(s):

Image Processing ◽

Statistical Analysis ◽

Early Detection ◽

Visual Inspection ◽

Causes Of Death ◽

Cure Rate ◽

Blood Disorders ◽

Detection And Diagnosis ◽

The Moment ◽

Early Detection And Diagnosis

Leukemia disease is one of the leading causes of death among human. Its cure rate and prognosis depends mainly on the early detection and diagnosis of the disease. At the moment, identification of blood disorders is through visual inspection of microscopic images by examining changes like texture, geometry, colour and statistical analysis of images . This project aims to preliminary of developing a detection of leukemia types using microscopic blood sample using MATLAB. Images are used as they are cheap and do not expensive for testing and lab equipment.

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Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors

Higher Education Studies ◽

10.5539/hes.v7n3p42 ◽

2017 ◽

Vol 7 (3) ◽

pp. 42

Author(s):

Vikash Rowtho

Keyword(s):

At Risk ◽

Academic Performance ◽

Early Detection ◽

Undergraduate Students ◽

At Risk Students ◽

Regression Analyses ◽

Linear Discriminant ◽

Student Dropout ◽

Performance Predictors ◽

A New Technique

Undergraduate student dropout is gradually becoming a global problem and the 39 Small Islands Developing States (SIDS) are no exception to this trend. The purpose of this research was to develop a method that can be used for early detection of students who are at-risk of performing poorly in their undergraduate studies. A sample of 279 students participated in the study conducted in a Mauritian private tertiary academic institution. Results of regression analyses identified the variables having a significant influence on academic performance. These variables were used in a linear discriminant analysis where 74 percent of the students could be correctly classified into three categories: at-risk, pass or fail. In conclusion, this study has proposed a new technique that can be used by institutions to determine significant academic performance predictors and then identify at-risk students upon whom interventions can be implemented prior to exams to address the problem of dropouts.

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Image processing for cervical pathology diagnosis using cervix’s polarized images

Science and Technology Development Journal ◽

10.32508/stdj.v20ik3.1086 ◽

2017 ◽

Vol 20 (K3) ◽

pp. 31-37

Author(s):

Tien Van Tran ◽

Cat Ngoc Phuong Phan ◽

Linh Quang Huynh ◽

Quynh Ngoc Nguyen ◽

Hieu Trung Nguyen

Keyword(s):

Quality Of Life ◽

Image Processing ◽

Early Detection ◽

Trichomonas Vaginalis ◽

Screening Methods ◽

Treatment Plans ◽

Suitable Treatment ◽

Image Processing Algorithms ◽

Processing Algorithms

Cervical pathologies are frequently occuring diseases and may affect women’s quality of life in many ways. These pathologies are curable with early detection and with a following suitable treatment plans. Colposcopy is a standard examination among screening methods which are used to early detect the abnormal lesions on cervix’s surface. Recently, studies about processing polarized image show ability to support diagnosis of the cervix. In this research, we use cervix’s polarized images and image processing algorithms to segment the blood distribution of Nabothian cyst and Trichomonas vaginalis infection. These results have the potential to provide underlying information of the cervix to support the diagnosis.

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Retinal vessel feature extraction from fundus image using image processing techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8892 ◽

2018 ◽

Vol 7 (2) ◽

pp. 687

Author(s):

R. Lavanya ◽

G. K. Rajini ◽

G. Vidhya Sagar

Keyword(s):

Image Processing ◽

Diabetic Retinopathy ◽

Early Detection ◽

Blood Vessels ◽

Retinal Vessel ◽

Retinal Images ◽

Image Processing Techniques ◽

Vessel Detection ◽

Proper Diagnosis ◽

Processing Techniques

Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood vessels in the retina become weakened and leak, forming small hemorrhages. As the disease progress, blood vessels may block, and sometimes leads to permanent vision loss. To help Clinicians in diagnosis of diabetic retinopathy in retinal images with an early detection of abnormalities with automated tools.Methods: Fundus photography is an imaging technology used to capture retinal images in diabetic patient through fundus camera. Adaptive Thresholding is used as pre-processing techniques to increase the contrast, and filters are applied to enhance the image quality. Morphological processing is used to detect the shape of blood vessels as they are nonlinear in nature.Results: Image features like, Mean and Standard deviation and entropy, for textural analysis of image with Gray Level Co-occurrence Matrix features like contrast and Energy are calculated for detected vessels.Conclusion: In diabetic patients eyes are affected severely compared to other organs. Early detection of vessel structure in retinal images with computer assisted tools may assist Clinicians for proper diagnosis and pathology.

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Digital Image Processing and Data Retrieval : The Needs

International Astronomical Union Colloquium ◽

10.1017/s0252921100082762 ◽

1982 ◽

Vol 64 ◽

pp. 109-110

Author(s):

A. Bijaoui

Keyword(s):

Image Processing ◽

Information Extraction ◽

Noise Reduction ◽

Contrast Enhancement ◽

Digital Image Processing ◽

Digital Image ◽

Data Retrieval

During the last decenny, Digital Image Processing (D.I.P.) has been introduced in astronomical studies to allow the information extraction.In a first step, D.I.P. has been used essentially to provide enhanced images (noise reduction, deconvolution, contrast enhancement), to reduce geometrical or photometrical distorsions and to extract rough data. So, a few reference date are needed (some comparison lines for example).

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Bio-Medical Image Processing

Examining Fractal Image Processing and Analysis - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0066-8.ch007 ◽

2020 ◽

pp. 158-169

Author(s):

Rasmita Lenka ◽

Koustav Dutta ◽

Ashimananda Khandual ◽

Soumya Ranjan Nayak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Early Detection ◽

Digital Image Processing ◽

Medical Science ◽

Medical Image Processing ◽

The World ◽

Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

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