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| 2. |
- Gelzinis, Adas, et al.
(författare)
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Categorizing laryngeal images for decision support
- 2007
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Ingår i: Advanced Concepts for Intelligent Vision Systems. - Berlin : Springer Berlin/Heidelberg. - 9783540746065 ; , s. 521-530
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Bokkapitel (refereegranskat)abstract
- This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.
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| 3. |
- Gelzinis, Adas, et al.
(författare)
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Increasing the discrimination power of the co-occurrence matrix-based features
- 2007
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Ingår i: Pattern Recognition. - Oxford : Pergamon Press. - 0031-3203 .- 1873-5142. ; 40:9, s. 2367-2372
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with an approach to exploiting information available from the co-occurrence matrices computed for different distance parameter values. A polynomial of degree n is fitted to each of 14 Haralick's coefficients computed from the average co-occurrence matrices evaluated for several distance parameter values. Parameters of the polynomials constitute a set of new features. The experimental investigations performed substantiated the usefulness of the approach.
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| 4. |
- Valinicius, D., et al.
(författare)
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Evolving Committees of Support Vector Machines
- 2007
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Ingår i: Machine Learning and Data Mining in Pattern Recognition, Proceedings. - Berlin : Springer Berlin/Heidelberg. - 9783540734987 ; , s. 263-275
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Konferensbidrag (refereegranskat)abstract
- The main emphasis of the technique developed in this work for evolving committees of support vector machines (SVM) is on a two phase procedure to select salient features. In the first phase, clearly redundant features are eliminated based on the paired t-test comparing the SVM output sensitivity-based saliency of the candidate and the noise feature. In the second phase, the genetic search integrating the steps of training, aggregation of committee members, and hyper-parameter as well as feature selection into the same learning process is employed. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real world problems have shown that significant improvements in correct classification rate can be obtained in a small number of iterations if compared to the case of using all the features available.
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| 5. |
- Verikas, Antanas, 1951-, et al.
(författare)
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A kernel-based approach to categorizing laryngeal images
- 2007
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Ingår i: Computerized Medical Imaging and Graphics. - New York : Pergamon Press. - 0895-6111 .- 1879-0771. ; 31:8, s. 587-594
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.
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| 6. |
- Verikas, Antanas, et al.
(författare)
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Estimating the amount of cyan, magenta, yellow, and black inks in arbitrary colour pictures
- 2007
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Ingår i: Neural Computing & Applications. - London : Springer London. - 0941-0643 .- 1433-3058. ; 16:2, s. 187-195
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with the offset lithographic colour printing. To obtain high quality colour prints, given proportions of cyan (C), magenta (M), yellow (Y), and black (K) inks (four primary inks used in the printing process) should be accurately maintained in any area of the printed picture. To accomplish the task, the press operator needs to measure the printed result for assessing the proportions and use the measurement results to reduce the colour deviations. Specially designed colour bars are usually printed to enable the measurements. This paper presents an approach to estimate the proportions directly in colour pictures without using any dedicated areas. The proportions—the average amount of C, M, Y, and K inks in the area of interest—are estimated from the CCD colour camera RGB (L*a*b*) values recorded from that area. The local kernel ridge regression and the support vector regression are combined for obtaining the desired mapping L*a*b* ⇒ CMYK, which can be multi-valued.
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| 7. |
- Verikas, Antanas, et al.
(författare)
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Multiple feature sets based categorization of laryngeal images
- 2007
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Ingår i: Computer Methods and Programs in Biomedicine. - Amsterdam : Elsevier. - 0169-2607 .- 1872-7565. ; 85:3, s. 257-266
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with an automated analysis of laryngeal images aiming to categorize the images into three decision classes, namely healthy, nodular, and diffuse. The problem is treated as an image analysis and classification task. Aiming to obtain a comprehensive description of laryngeal images, multiple feature sets exploiting information on image colour, texture, geometry, image intensity gradient direction, and frequency content are extracted. A separate support vector machine (SVM) is used to categorize features of each type into the decision classes. The final image categorization is then obtained based on the decisions provided by a committee of support vector machines. Bearing in mind a high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 laryngeal images recorded at the Department of Otolaryngology, Kaunas University of Medicine is rather promising.
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