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1

A practical pretreatment planning method of multiple puncturing for thermal ablation surgery

Li Jing, Xu Yuanqi, Shen Nanyan, Feng Lanyun, Ran Zhuang, Deng Zongqian

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1469--1485

EN

Thermal ablation surgery serves as one of the main approaches to treat liver tumors. The pretreatment planning, which highly demands the experience and ability of the physician, plays a vital role in thermal ablation surgery. The planning of multiple puncturing is necessary for avoiding the possible interference, destroying the tumor thoroughly and minimizing the damage to healthy tissue. A GPU-independent pretreatment planning method is proposed based on multi-objective optimization, which takes the most comprehensive constraints into consideration. An adaptive decision method of closing kernel size based on Jenks Natural Breaks is utilized to describe the final feasible region more accurately. It should be noted that the reasonable procedure of solving the feasible region and the use of KD tree based high dimensional search approach are used to enhance the computational efficiency. Seven constraints are handled within 7 s without GPU acceleration. The Pareto front points of nine puncturing tests are obtained in 5 s by using the NSGA-II algorithm. To evaluate the maximum difference and similarity between the planning results and the puncturing points recommended by the physician, Hausdorff distance and overlap rate are respectively developed, the Hausdorff distances are within 30 mm in seven out of nine tests and the average value of overlap rate is 73.0% for all the tests. The puncturing paths of high safety and clinical-practice compliance can be provided by the proposed method, based on which the pretreatment planning software developed can apply to the interns' training and ability evaluating for thermal ablation surgery.

2

Impact of Image Preprocessing on Recognition of Letters of Sign Language

Abramowicz P., Topczewska M.

Advances in Computer Science Research

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2018

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Nr 14

5--16

EN

The article concerns the problem of the selected sign language letters in the form of images classification. The impact of the image preprocessing methods as adaptive thresholding or edge detection is tested. In addition, the influence of the found shapes filling is checked, as well as centering the hands on the images. The following classification methods were chosen: SVM classifier with linear kernel function, Naive Bayes and Random Forests. The accuracy, F-measure, the AUC, MAE and Kappa coefficient were reported as measures of classification quality.

PL

Artykuł dotyczy klasyfikacji wybranych liter alfabetu migowego w postaci obrazów. Badany jest wpływ na wyniki kilku metod przetwarzania wstępnego obrazów, w tym progowania adaptacyjnego oraz detekcji krawędzi. Dodatkowo sprawdzane jest wypełnianie znalezionych kształtów, a także centrowanie dłoni na obrazach. Jako metody klasyfikacji wybrane zostały: klasyfikator SVM z liniową funkcją jądrową, klasyfikator Naive Bayes oraz Random Forest. Jako miary jakości klasyfikacji raportowane są jakość klasyfikacji, miara F, pole pod krzywą ROC oraz współczynnik Kappa.

3

Study and adjustment of dermoscopic image processing algorithms for lesion border detection

Borys D., Szeremet M., Psiuk-Maksymowicz K., Ostrowski Z., Frąckiewicz M.

Studia Informatica

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2016

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Vol. 37, nr 3B

33-46

EN

The goal of our work was an initial preprocessing of dermoscopic images towards accurate lesion border detection. Four algorithms were proposed and analyzed: MS – algorithm using mean shift clustering, HE – algorithm using histogram equalization, TTH – algorithm using the top-hat transform, PCA – algorithm using principal component analysis. Those algorithms were tested on PH2 images database that contains 200 dermoscopic images, each with a mask of the lesion. Those algorithms were optimized using lesion mask from database and Jaccard index as a measure of similarity of both sets. Simple statistical analysis of indexes was used to compare proposed algorithms in term of their accuracy.

PL

W artykule poruszono problem wstępnego przetwarzania obrazów dermatoskopowych w celu znalezienia konturu znamienia. Zaproponowano i porównano cztery algorytmy: MS – wykorzystujący klasteryzację ‘mean shift’, HE – wykorzystujący wyrównywanie histogramu, TTH – wykorzystujący transformację ‘top-hat’, PCA – wykorzystujący metodę analizy głównych składowych. Algorytmy przetestowano z wykorzystaniem obrazów z bazy PH2, zawierającej 200 obrazów wraz z obrysem ręcznym, a ich parametry dobrano optymalizując indeks Jaccarda. Proste statystyki wyników pozwoliły na porównanie proponowanych algorytmów.

4

Porównanie wybranych miar kontrastu obrazów achromatycznych

Bal A.

Pomiary Automatyka Kontrola

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2010

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R. 56, nr 11

1359-1353

PL

W pracy poruszono zagadnienie oceny kontrastu achromatycznych obrazów cyfrowych. Przedstawiono porównanie wyników oceny kontrastu obrazów przy wykorzystaniu, zarówno znanych z literatury jak i nowych, miar kontrastu. Zaproponowane nowe miary bazują na lokalnej realizacji pewnych znanych z literatury globalnych miar kontrastu. Badania przeprowadzono dla kilkudziesięciu obrazów testowych. Przedstawiono także problemy związane z doborem miar kontrastu do danego zastosowania.

EN

The paper deals with the problem of gray level image contrast evaluation. Comparison of several, both known from the literature and new, image contras evaluation methods are presented. Methods taken into consideration are: (1) and (2)-two methods based on Weber-Fechner law, (3) and (4)-variance based methods, method (5) with its modifications-based on difference between gray-level values of neighboring pixels, (6)-image energy based method, (7)- method based on Shannon entropy information theory, (8)-(11)-group of new methods based on the idea of aggregating of local contrast evaluation value obtained for neighborhood of pixel for all pixels in image. The comparison was performed on the set of 48 test images obtained as a result of contrast enhancement of 8 images (Fig. 1) using 5 contrast enhancement methods. The results of contrast evaluation of all images are given in Table 1. In order to compare the numerical contrast evaluation with visual judgment, two subsets of test images are presented (Figs. 2 and 3) together with charts of the contrast values (Fig. 4). The obtained results are analysed in Section 4, where on the example of two different applications of contrast enhancement the difficulties with contrast evaluation and selection of the best contrast evaluation method are discussed. It is not possible to choose the best contrast evaluation method basing on the obtained results and taking into consideration different purposes of image contrast enhancement. Because of this, a contrast evaluation method should be chosen for each application individually.

5

Porównanie wybranych metod oceny kontrastu achromatycznych obrazów cyfrowych

Bal A.

Pomiary Automatyka Kontrola

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2009

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R. 55, nr 7

501-503

PL

Praca dotyczy zagadnienia oceny kontrastu achromatycznych obrazów cyfrowych. Przedstawiono i porównano znane z literatury metody oceny kontrastu obrazów, a także zaproponowano nowe rozwiązania bazujące na określeniu różnic poziomów szarości sąsiadujących ze sobą pikseli. Ocena przydatności poszczególnych metod została dokonana na przykładzie zadania wyboru najlepszej wersji obrazu pod kątem jego dalszej analizy. Przeprowadzona analiza wyników oceny kontrastu obrazów wskazuje, że dla przyjętych warunków porównania najlepszą metodą oceny kontrastu jest jedna z nowych metod.

EN

This paper deals with the gray-level image contrast evaluation problem. In the paper some methods of contrast evaluation are considered, namely: (1) and (2) - two methods based on Weber-Fechner law ((2) is a Michelson contrast), next two methods (3) and (4) are based on variance of gray-level values in the image, and the last two (5) and (6) are new methods in which the difference between gray-level values of neighbouring pixels is used for calculating the image contrast ratio. The evaluation of the described methods has been made for one of the standard usage cases of the contrast evaluation method, i.e. evaluation of images after contrast enhancement for choosing the best of them for its visual analysis. The investigations have been performed on the example of aerial image AERIAL (Fig. 1). The contrast enhancement of this image gives the set of test images (a)-(j) (Fig. 1). Table 1 presents the contrast enhancement methods and their parameters used for obtaining test images. The contrasts evaluation values of the test images obtained by use of the methods (1)-(6) are given in Table 2. Table 3 shows the ranking of the images (a)-(j) obtained by sorting in descending order the contrast ratio of test images independently for each evaluation method. It is difficult to indicate the best evaluation method but it seems that, basing on the comparison, the new method (5) is the best image contrast evaluation method