scholarly journals Curvelet Denoising with Improved Thresholds for Application on Ultrasound Images

2013 ◽  
pp. 156-161
Author(s):  
Tajinder Kaur ◽  
Dinesh Kumar ◽  
Ekta Walia ◽  
Manjit Sandhu
Keyword(s):  
Medical Image ◽  
Noise Suppression ◽  
Acoustic Noise ◽  
Medical Image Processing ◽  
Curvelet Transform ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Challenging Problem ◽  
Effective Suppression ◽  
Ultrasonic Images

In medical image processing, image denoising has become a very essential exercise all through the diagnose. Negotiation between the preservation of useful diagnostic information and noise suppression must be treasured in medical images. In case of ultrasonic images a special type of acoustic noise, technically known as speckle noise, is the major factor of image quality degradation. Many denoising techniques have been proposed for effective suppression of speckle noise. Removing noise from the original image or signal is still a challenging problem for researchers. In this paper, a Curvelet transform based denoising with improved thresholds is proposed for ultrasound images.

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

2013 ◽  
Vol 760-762 ◽  
pp. 1552-1555 ◽  
Author(s):  
Jing Jing Wang ◽  
Xiao Wei Song ◽  
Mei Fang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Region Growing ◽  
Challenging Problem ◽  
Correct Judgment ◽  
Threshold Segmentation ◽  
Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

scholarly journals Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

2021 ◽  
pp. 80-98
Author(s):  
Siji Jose Pulluparambil ◽  
Subrahmanya Bhat
Keyword(s):  
Image Processing ◽  
Literature Review ◽  
Medical Image ◽  
Polycystic Ovary ◽  
Detection System ◽  
Medical Image Processing ◽  
Data Sets ◽  
Ultrasound Images ◽  
Effective Diagnosis ◽  
Reproductive Aged Women

Purpose: Considered as the most common hormonal disorder among women, polycystic ovary syndrome or PCOS affects 1 in 10 reproductive aged women (18 - 44 years). Ultrasonography is applied for assessing the ovaries to detect PCOS. The patients affected by PCOS consist of 10-12 cysts present in the ovary, but more than 10 cysts are more enough to diagnose the disorder from the ultrasound images. Then, by examining the ultrasound the presence of follicles will be determined. Therefore, the image processing approaches have assisted to identify the characteristics like follicle size, number of follicles and structure to minimize the workload and time of doctors. PCOS do not have better treatment and effective diagnosis. This paper includes reviewing a summary of some of the researches that have been going in area of medical diagnosis. Based on the review, research gap, research agendas to carry out further research are identified. Approach: A detailed study on the algorithms used in medical image processing and classification. Findings: The study indicated that most of the classification of polycystic ovarian syndrome is done merely on the clinical data sets. The new hybrid methodology proposed will be more precise as both images and lifestyle are analysed. Originality: The type of data required for detection system are studied and the architecture and schematic diagram of a proposed system are included. Paper Type: Literature Review.

scholarly journals M2 FILTER FOR SPECKLE NOISE SUPPRESSION IN BREAST ULTRASOUND IMAGES

2015 ◽  
Vol 06 (02) ◽  
pp. 1137-1144 ◽  
Author(s):  
Samundeeswari E.S. ◽  
◽  
Saranya P.K. ◽  
Manavalan R. ◽  
◽  
...  
Keyword(s):  
Noise Suppression ◽  
Speckle Noise ◽  
Breast Ultrasound ◽  
Ultrasound Images

scholarly journals Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Karthik Kalyan ◽  
Binal Jakhia ◽  
Ramachandra Dattatraya Lele ◽  
Mukund Joshi ◽  
Abhay Chowdhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Speckle Noise ◽  
Training Data ◽  
Ultrasound Images ◽  
Data Set ◽  
Imaging Artifacts ◽  
Artificial Neural ◽  
Ultrasonic Images

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

scholarly journals A review on analysis and diagnosis of diseases like tumor using medical image processing techniques

2020 ◽  
Author(s):  
Arachana Khandait ◽  
Ashish Panat ◽  
Ashish Panat
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Medical Image ◽  
Image Quality Assessment ◽  
Processing System ◽  
Medical Image Processing ◽  
Dynamic Monitoring ◽  
Challenging Problem ◽  
Image Processing Techniques ◽  
Processing Techniques

The field of image processing, image quality assessment is a fundamental and challenging problem with many interests in a variety of applications. Dynamic monitoring, adjusting image quality, optimizing algorithms and parameter settings of image processing systems are benchmarking in image processing system and algorithms. In this paper using such techniques we are going to analysis the detection of diseases like tumour for the ease in medical use.

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