scholarly journals Incorporating Domain Knowledge into Medical Image Mining

10.5772/50207 ◽  
2012 ◽  
Author(s):  
Haiwei Pan
Keyword(s):  
Medical Image ◽  
Domain Knowledge ◽  
Image Mining

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

scholarly journals Knowledge Vector Representation of Three Dimensional Convex Polyhedrons and Reconstruction of Medical Images using Knowledge Vector

2021 ◽  
Author(s):  
Shilpa Rani ◽  
Kamlesh Lakhwani ◽  
Sandeep Kumar
Keyword(s):  
Medical Image ◽  
Domain Knowledge ◽  
Three Dimensional ◽  
3D Image ◽  
Clinical Value ◽  
Image Construction ◽  
3D Image Reconstruction ◽  
Syntactic Pattern ◽  
Important Approach ◽  
One Step

Abstract Three-dimensional image construction and reconstruction play an important role in various applications of the real world in the field of computer vision. In the last three decades, researchers are continually working in this area because construction and reconstruction is an important approach in medical imaging. Reconstruction of the 3D image allows us to find the lesion information of the patients which could offer a new and accurate approach for the diagnosis of the disease and it adds a clinical value. Considering this, we proposed novel approaches for the construction and reconstruction of the image. First, the novel construction algorithm is used to extract the features from an image using syntactic pattern recognition. The proposed algorithm is able to extract in-depth features in all possible directions and planes and also able to represent the 3D image into a textual form. These features vector is nothing but a string that consists of direction and length information in syntactic form. For the identification of syntactic grammar, a real 3D clay model was made and identified the different possible patterns in the image. According to the domain knowledge, in a 3D image, a pixel could be present in 26 possible directions and we incorporated all possible directions in the proposed algorithm. In the same way, for the reconstruction of the image novel algorithm is proposed. In this algorithm, the knowledge vector has been taken as an input and the algorithm is able to reconstruct a 3D image. Reconstruction allows us to explore the internal details of the 3D images such as the size, shape, and structure of the object which could take us one step ahead in the field of medical image processing. Performances of the proposed algorithms are evaluated on five medical image dataset and the datasets are collected from Pentagram research institute, Hyderabad and results are outperformed in real-time. The accuracy of the proposed method is 94.78% and the average execution time is 6.76 seconds which is better than state of art methods.

scholarly journals Medical domain knowledge in domain-agnostic generative AI

2022 ◽  
Author(s):  
Jakob Nikolas Kather ◽  
Narmin Ghaffari Laleh ◽  
Sebastian Foersch ◽  
Daniel Truhn
Keyword(s):  
Medical Image ◽  
Domain Knowledge ◽  
Medical Image Analysis ◽  
Medical Knowledge ◽  
Fine Tuning ◽  
Domain Specific ◽  
Additional Domain ◽  
Single Use ◽  
Explicit Training ◽  
Medical Concepts

The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state of the art in text-to-image generative artificial intelligence (AI). GLIDE has rich representations, but medical applications of this model have not been systematically explored. If GLIDE had useful medical knowledge, it could be used for medical image analysis tasks, a domain in which AI systems are still highly engineered towards a single use-case. Here we show that the publicly available GLIDE model has reasonably strong representations of key topics in cancer research and oncology, in particular the general style of histopathology images and multiple facets of diseases, pathological processes and laboratory assays. However, GLIDE seems to lack useful representations of the style and content of radiology data. Our findings demonstrate that domain-agnostic generative AI models can learn relevant medical concepts without explicit training. Thus, GLIDE and similar models might be useful for medical image processing tasks in the future - particularly with additional domain-specific fine-tuning.

Medical Image Mining Using Fuzzy Connectedness Image Segmentation

2016 ◽  
pp. 184-209
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.

From spoken narratives to domain knowledge: Mining linguistic data for medical image understanding

2014 ◽  
Vol 62 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Xuan Guo ◽  
Qi Yu ◽  
Cecilia Ovesdotter Alm ◽  
Cara Calvelli ◽  
Jeff B. Pelz ◽  
...  
Keyword(s):  
Medical Image ◽  
Domain Knowledge ◽  
Image Understanding ◽  
Knowledge Mining ◽  
Linguistic Data ◽  
Medical Image Understanding
Sign in / Sign up

Export Citation Format

Share Document