Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images

Medical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of medical image feature extraction, there are some problems, such as inconspicuous feature extraction and low feature preparation rate. Combined with the learning idea of convolution neural network, the image multifeature vectors are quantized in a deeper level, which makes the image features further abstract and not only makes up for the one-sidedness of single feature description but also improves the robustness of feature descriptors. This paper presents a medical image processing method based on multifeature fusion, which has high feature extraction effect on medical images of chest, lung, brain and liver, and can better express the feature relationship of medical images. Experimental results show that the accuracy of the proposed method is more than 5% higher than that of other methods, which shows that the performance of the proposed method is better.

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Comparison of Different Edge Detection Techniques to Improve Quality of Medical Images

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8921 ◽

2020 ◽

Vol 17 (6) ◽

pp. 2496-2507

Author(s):

Mohit Chhabra ◽

Rajneesh Kumar

Keyword(s):

Feature Extraction ◽

Mean Square Error ◽

Medical Images ◽

Research Work ◽

Absolute Error ◽

Quality Parameters ◽

Image Feature ◽

Mean Square ◽

Extraction Techniques ◽

Detection Techniques

In modern era the major challenge will betodetect diseases from Medical Images. To curb this challenge, different efficient image feature extraction techniques was required in medical fields. Today Medical field industry today deals with millions of images of different disease of brain heart, lungs. So in this paper, we had presented a comparison among different feature extraction technique like Canny, Laplacian of Gaussian, Sobel, Prewit on large number of images of lung disease. The objective of our research work was to find best extraction techniques based on various image quality parameters such as Mean absolute error (MAE), Root mean square error (RMSE), mean square error (MSE), Signal to noise ratio (SNR).

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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