Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

2012 ◽  
pp. 23-32
Author(s):  
Aswini Kumar Mohanty ◽  
P. K. Champati ◽  
Manas Rajan Senapati ◽  
Saroj Kumar Lena
Keyword(s):  
Image Mining ◽  
Complete Field ◽  
Mining Methods

Image Mining for the Construction of Semantic-Inference Rules and for the Development of Automatic Image Diagnosis Systems

2011 ◽  
pp. 682-704
Author(s):  
Petra Perner
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Digital Image ◽  
Image Understanding ◽  
Data Representation ◽  
Inference Rules ◽  
Image Mining ◽  
Semantic Inference ◽  
Mining Methods ◽  
Image Representations

This chapter introduces image mining as a method to discover implicit, previously unknown and potentially useful information from digital image and video repositories. It argues that image mining is a special discipline because of the special type of data and therefore, image-mining methods that consider the special data representation and the different aspects of image mining have to be developed. Furthermore, a bridge has to be established between image mining and image processing, feature extraction and image understanding since the later topics are concerned with the development of methods for the automatic extraction of higher-level image representations. We introduce our methodology, the developed methods and the system for image mining which we successfully applied to several medical image-diagnostic tasks.

From words to pixels: text and image mining methods for service research

2019 ◽  
Vol 30 (5) ◽  
pp. 593-620 ◽  
Author(s):  
Francisco Villarroel Ordenes ◽  
Shunyuan Zhang
Keyword(s):  
Text Mining ◽  
Service Research ◽  
Frontline Employees ◽  
Image Mining ◽  
Text And Image ◽  
Research Directions ◽  
Content Type ◽  
Customer Reviews ◽  
Marketing Operations ◽  
Mining Methods

Purpose The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research. Design/methodology/approach On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided. Findings The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services. Research limitations/implications This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods. Practical implications The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience. Originality/value The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

Image Mining for the Construction of Semantic-Inference Rules and for the Development of Automatic Image Diagnosis Systems

2011 ◽  
pp. 75-97
Author(s):  
Petra Perner
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Digital Image ◽  
Image Understanding ◽  
Data Representation ◽  
Inference Rules ◽  
Image Mining ◽  
Semantic Inference ◽  
Mining Methods ◽  
Image Representations

This chapter introduces image mining as a method to discover implicit, previously unknown and potentially useful information from digital image and video repositories. It argues that image mining is a special discipline because of the special type of data and therefore, image-mining methods that consider the special data representation and the different aspects of image mining have to be developed. Furthermore, a bridge has to be established between image mining and image processing, feature extraction and image understanding since the later topics are concerned with the development of methods for the automatic extraction of higher-level image representations. We introduce our methodology, the developed methods and the system for image mining which we successfully applied to several medical image-diagnostic tasks.

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.

THE MODEL FOR THE ANALYSIS OF PHARMACOEPIDEMIOLOGICAL DATA BASED ON DATA MINING METHODS

2017 ◽  
Vol 19 (10) ◽  
pp. 289-298
Author(s):  
I.M. Burykin ◽  
◽  
G.N. Aleeva ◽  
R.Kh. Khafizianova ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Mining Methods

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

2019 ◽  
Vol 15 (8) ◽  
pp. 769-776
Author(s):  
Kalaivani Subramani ◽  
Shantharajah Periyasamy ◽  
Padma Theagarajan
Keyword(s):  
Pattern Matching ◽  
Error Rate ◽  
Median Filter ◽  
Identification System ◽  
Double Line ◽  
Cultivation System ◽  
Matching Algorithm ◽  
Non Local ◽  
Mining Methods ◽  
Blight Disease

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

A Systematic Assessment of Numerical Association Rule Mining Methods

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Minakshi Kaushik ◽  
Rahul Sharma ◽  
Sijo Arakkal Peious ◽  
Mahtab Shahin ◽  
Sadok Ben Yahia ◽  
...  
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Systematic Assessment ◽  
Mining Methods

Massive picture retrieval system based on big data image mining

2021 ◽  
Vol 121 ◽  
pp. 54-58
Author(s):  
Kun Zhang ◽  
Kai Chen ◽  
Binghui Fan
Keyword(s):  
Big Data ◽  
Retrieval System ◽  
Image Mining

Research on diagnostic strategy for faults in VRF air conditioning system using hybrid data mining methods

2021 ◽  
pp. 111144
Author(s):  
Yuzhou Wang ◽  
Zhengfei Li ◽  
Huanxin Chen ◽  
Jianxin Zhang ◽  
Qian Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Air Conditioning ◽  
Diagnostic Strategy ◽  
Air Conditioning System ◽  
Hybrid Data ◽  
Mining Methods
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