59 research outputs found
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
Multi-class Multi-label Classification and Detection of Lumbar Intervertebral Disc Degeneration MR Images using Decision Tree Classifiers
Evidence-based medicine decision-making based on computer-aided methods is a new direction in modernhealthcare. Data Mining Techniques in Computer-Aided Diagnosis (CAD) are powerful and widely used toolsfor efficient and automated classification, retrieval, and pattern recognition of medical images. They becomehighly desirable for the healthcare providers because of the massive and increasing volume of intervertebral discdegeneration images. A fast and efficient classification and retrieval system using query images with high degreeof accuracy is vital. The method proposed in this paper for automatic detection and classification of lumbarintervertebral disc degeneration MRI-T2 images makes use of texture-based pattern recognition in data mining.A dataset of 181segmented ROIs, corresponding to 89 normal and 92 degenerated (narrowed) discs at differentvertebral level, was analyzed and textural features (contrast, entropy, and energy) were extracted from each disc-ROI. The extracted features were employed in the design of a pattern recognition system using C4.5 decisiontree classifier. The system achieved a classification accuracy of 93.33% in designing a Multi-class Multi-labelclassification system based on the affected disc position. This work combined with its higher accuracy isconsidered a valuable knowledge for orthopedists in their diagnosis of lumbar intervertebral disc degeneration inT2-weighted Magnetic Resonance sagittal Images and for automated annotation, archiving, and retrieval of theseimages for later on usage.Keywords: Data Mining, Image Processing, Lumbar Intervertebral Disc Degeneration, MRI-T2, Decision Trees,Multi-class Multi-label Classification
Implementasi Teknik Data Mining untuk Sistem Pemindai Barang di Bandara
Saat ini, bandara menjadi salah satu tempat yang sangat vital dan harus memiliki tingkat
keamanan yang sangat tinggi Permasalahan utama yang timbul ialah bagaimana
mengoptimalakan sistem pengamanan bandara yang telah ada sehingga mampu
meningkatkan keamanan dan kenyamanan calon penumpang pesawat. Pada paper ini
kami menyajikan sebuah ide untuk meningkatkan keamanan bandara dengan penerapan
image mining pada mesin pindai sebagai pemberitahuan awal apabila terdapat barang
yang mencurigakan. Dalam tulisan ini, kami melakukan beberapa tahapan terhadap
database citra dari hasil pindai antara lain melakukan ekstraksi pada fitur untuk
mengetahui pola dari barang berbahaya yang ada. Pola tadi akan dimasukkan kedalam
lookup table berdasarkan kriteria atau tingkatan yang diinginkan
The Usage of Association Rule Mining to Identify Influencing Factors on Deafness After Birth
Background: Providing complete and high quality health care services has very important role to enable
people to understand the factors related to personal and social health and to make decision regarding
choice of suitable healthy behaviors in order to achieve healthy life. For this reason, demographic and
clinical data of person are collecting, this huge volume of data can be known as a valuable resource for
analyzing, exploring and discovering valuable information and communication. This study using forum
rules techniques in the data mining has tried to identify the affecting factors on hearing loss after
birth in Iran. Materials and Methods: The survey is kind of data oriented study. The population of the
study is contained questionnaires in several provinces of the country. First, all data of questionnaire
was implemented in the form of information table in Software SQL Server and followed by Data Entry
using written software of C # .Net, then algorithm Association in SQL Server Data Tools software and
Clementine software was implemented to determine the rules and hidden patterns in the gathered
data. Findings: Two factors of number of deaf brothers and the degree of consanguinity of the parents
have a significant impact on severity of deafness of individuals. Also, when the severity of hearing loss
is greater than or equal to moderately severe hearing loss, people use hearing aids and Men are also
less interested in the use of hearing aids. Conclusion: In fact, it can be said that in families with consanguineous
marriage of parents that are from first degree (girl/boy cousins) and 2nd degree relatives
(girl/boy cousins) and especially from first degree, the number of people with severe hearing loss or
deafness are more and in the use of hearing aids, gender of the patient is more important than the
severity of the hearing los
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