Henryk Palus | Silesian University of Technology (original) (raw)

Papers by Henryk Palus

Zeszyty Naukowe. Automatyka / Politechnika Śląska, 2000

Przegląd Elektrotechniczny, 2008

Przegląd Elektrotechniczny, 2008

In this paper the postprocessing procedures adapted to the region-based colour image segmentation... more In this paper the postprocessing procedures adapted to the region-based colour image segmentation, represented by unseeded region growing technique, are presented. The removal of small regions from segmented images by merging them to the neighbouring regions with the largest areas was particularly useful. The possibility of using this procedure to removing of highlights that disturbing segmentation process, has been proved. (Postprocessing in region-based colour image segmentation).

Przegląd Elektrotechniczny, 2008

In this paper we show, from image processing point of view, different methods of computing of col... more In this paper we show, from image processing point of view, different methods of computing of colourfulness of the image. We have calculated the colourfulness using simple estimate based on statistical parameters of the pixel cloud along red-green and yellow-blue axes. All experiments have been carried out on the set of natural colour images with different perceptual colourfulness. The relationships between colourfulness of the image and perceptual attributes (H, L, S) of pixels have been experimentally investigated. During image processing the colourfulness of the image can be changed but sometimes it should be preserved e.g. in image filtering. We have presented it on examples, which show that the difference of colourfulness can be useful for evaluating the image filtering algorithms beside such traditional performance functions as PSNR and DeltaE

International Journal of Applied Mathematics and Computer Science, Mar 1, 2011

In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour ima... more In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The k-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed by a postprocessing procedure. Experimental tests realized on real endoscopic colour images show the superiority of KHM over KM.

Proceedings of SPIE, Dec 8, 2015

Color quantization is still an important auxiliary operation in the processing of color images. T... more Color quantization is still an important auxiliary operation in the processing of color images. The K-means clustering (KM), used to quantize the color, requires an appropriate initialization. In this paper, we propose a combined KM method that use to initialize the results of well-known quantization algorithms such as Wu's, NeuQuant (NQ) and Neural Gas (NG). This approach, assessed by three quality indices: PSNR, ΔE and ΔM, improves the results. Experimental results of such combined quantization indicate that the deterministic Wu+KM and random NG+KM approaches leading to the best quantized images.

Proceedings of SPIE, Feb 12, 2015

Image segmentation is one of the most difficult steps in the computer vision process. Pixel clust... more Image segmentation is one of the most difficult steps in the computer vision process. Pixel clustering is only one among many techniques used in image segmentation. In this paper is proposed a new segmentation technique, making clustering in the five-dimensional feature space built from three color components and two spatial coordinates. The advantages of taking into account the information about the image structure in pixel clustering are shown. The proposed 5D k-means technique requires, similarly to other segmentation techniques, an additional postprocessing to eliminate oversegmentation. Our approach is evaluated on different simple and complex images.

Proceedings of SPIE, Mar 17, 2017

Color image quantization is an often used in such tasks as image compression and image segmentati... more Color image quantization is an often used in such tasks as image compression and image segmentation. In the paper, we continue to consider the usefulness of the new DSCSI metric for evaluating quantized images. Our use of the DSCSI metric confirmed that the color quantization in the CIELAB color space is better than in the basic RGB color space. On several examples we found very good DSCSI suitability in the case of quantization with dithering. During the tests of different dithering algorithms the best results, in terms of DSCSI metric, reached the classical Floyd-Steinberg algorithm at error propagation level of 75-85%.

Bulletin of The Polish Academy of Sciences-technical Sciences, 2018

Ultrasonography has proved its usefulness in the evaluation of joint inflammations caused by rheu... more Ultrasonography has proved its usefulness in the evaluation of joint inflammations caused by rheumatoid arthritis. The illness severity is scored by human examiners based on their experience, but some discrepancies in the final diagnosis and treatment frequently occur. Therefore, the main aim of this work is the elaboration of an automatic method of the localization of finger joint inflammation level in ultrasound images. In this paper we propose a novel, fully automated framework for synovitis region segmentation. In our approach we compare several bones and joint localization methods based on the seeded region growing technique, which is combined with different speckle noise filtering algorithms. This technique extracts a region from the image using some predefined criteria of similarity between initially selected point and the pixels in its neighborhood. The seed point is localized automatically as the darkest patch within a small region between two detected finger bones close to the joint. The region affected by synovitis is found using the adopted criterion of homogeneity based on a patch to patch similarity measure. The obtained results exhibit a satisfying accuracy in comparison with the annotations prepared by an expert and the results delivered by semi-automatic methods that require manual bones delineation.

Symmetry, Aug 1, 2019

Color image quantization has become an important operation often used in tasks of color image pro... more Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu's algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.

Proceedings of SPIE, Mar 17, 2017

Color quantization is an important operation in the field of color image processing. In this pape... more Color quantization is an important operation in the field of color image processing. In this paper, we consider a usefulness of the new DSCSI metric to assessment of quantized images. This metric is shown in the background of other useful image quality metrics to evaluate the color image differences and it has also been proven that DSCSI metric achieves the highest correlation coefficients with MOS. For further veriffcation DSCSI metric the combined methods that use to initialize the results of well-known splitting algorithms such as POP, MC, Wu etc. were tested. Experimental results of such combined methods indicate that the Wu+KM approach leading to the best quantized images in the sense of DSCSI metric.

Pomiary Automatyka Kontrola, 2007

Breast cancer is one of the most common cancers in women. Typically, the course of the disease is... more Breast cancer is one of the most common cancers in women. Typically, the course of the disease is asymptomatic in the early stages of breast cancer. Imaging breast examinations allow early detection of the cancer, which is associated with increased chances of a complete cure. There are many breast imaging techniques such as: mammography (MM), ultrasound imaging (US), positron-emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI). These imaging techniques differ in terms of effectiveness, price, type of physical phenomenon, the impact on the patient and its availability. In this paper, we focus on MRI imaging and we compare three breast lesion segmentation algorithms that have been tested on QIN Breast DCE-MRI database, which is publicly available. The obtained values of Dice and Jaccard indices indicate the segmentation using k-means algorithm.

Sensors, Jan 18, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Sensors, Aug 12, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The aim of this work was to test the most popular and essential algorithms of the intensity nonun... more The aim of this work was to test the most popular and essential algorithms of the intensity nonuniformity correction of the breast MRI imaging. In this type of MRI imaging, especially in the proximity of the coil, the signal is strong but also can produce some inhomogeneities. Evaluated methods of signal correction were: N3, N3FCM, N4, Nonparametric, and SPM. For testing purposes, a uniform phantom object was used to obtain test images using breast imaging MRI coil. To quantify the results, two measures were used: integral uniformity and standard deviation. For each algorithm minimum, average and maximum values of both evaluation factors have been calculated using the binary mask created for the phantom. In the result, two methods obtained the lowest values in these measures: N3FCM and N4, however, for the second method visually phantom was the most uniform after correction.

The main goal of colour quantization methods is a colour reduction with minimum colour error. In ... more The main goal of colour quantization methods is a colour reduction with minimum colour error. In this paper were investigated six following colour quantization techniques: the classical median cut, improved median cut, clustering k-means technique in two colour versions (RGB, CIELAB) and also two versions of relative novel technique named k-harmonic means. The comparison presented here was based on testing of ten natural colour images for quantization into 16, 64 and 256 colours. In evaluation process two criteria were used: the mean squared quantization error (MSE) and the average error in the CIELAB colour space (DeltaE). During tests the efficiency of k-harmonic means applied to colour quantization has been proved.

Zeszyty Naukowe. Automatyka / Politechnika Śląska, 2000

Przegląd Elektrotechniczny, 2008

Przegląd Elektrotechniczny, 2008

In this paper the postprocessing procedures adapted to the region-based colour image segmentation... more In this paper the postprocessing procedures adapted to the region-based colour image segmentation, represented by unseeded region growing technique, are presented. The removal of small regions from segmented images by merging them to the neighbouring regions with the largest areas was particularly useful. The possibility of using this procedure to removing of highlights that disturbing segmentation process, has been proved. (Postprocessing in region-based colour image segmentation).

Przegląd Elektrotechniczny, 2008

In this paper we show, from image processing point of view, different methods of computing of col... more In this paper we show, from image processing point of view, different methods of computing of colourfulness of the image. We have calculated the colourfulness using simple estimate based on statistical parameters of the pixel cloud along red-green and yellow-blue axes. All experiments have been carried out on the set of natural colour images with different perceptual colourfulness. The relationships between colourfulness of the image and perceptual attributes (H, L, S) of pixels have been experimentally investigated. During image processing the colourfulness of the image can be changed but sometimes it should be preserved e.g. in image filtering. We have presented it on examples, which show that the difference of colourfulness can be useful for evaluating the image filtering algorithms beside such traditional performance functions as PSNR and DeltaE

International Journal of Applied Mathematics and Computer Science, Mar 1, 2011

In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour ima... more In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The k-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed by a postprocessing procedure. Experimental tests realized on real endoscopic colour images show the superiority of KHM over KM.

Proceedings of SPIE, Dec 8, 2015

Color quantization is still an important auxiliary operation in the processing of color images. T... more Color quantization is still an important auxiliary operation in the processing of color images. The K-means clustering (KM), used to quantize the color, requires an appropriate initialization. In this paper, we propose a combined KM method that use to initialize the results of well-known quantization algorithms such as Wu's, NeuQuant (NQ) and Neural Gas (NG). This approach, assessed by three quality indices: PSNR, ΔE and ΔM, improves the results. Experimental results of such combined quantization indicate that the deterministic Wu+KM and random NG+KM approaches leading to the best quantized images.

Proceedings of SPIE, Feb 12, 2015

Image segmentation is one of the most difficult steps in the computer vision process. Pixel clust... more Image segmentation is one of the most difficult steps in the computer vision process. Pixel clustering is only one among many techniques used in image segmentation. In this paper is proposed a new segmentation technique, making clustering in the five-dimensional feature space built from three color components and two spatial coordinates. The advantages of taking into account the information about the image structure in pixel clustering are shown. The proposed 5D k-means technique requires, similarly to other segmentation techniques, an additional postprocessing to eliminate oversegmentation. Our approach is evaluated on different simple and complex images.

Proceedings of SPIE, Mar 17, 2017

Color image quantization is an often used in such tasks as image compression and image segmentati... more Color image quantization is an often used in such tasks as image compression and image segmentation. In the paper, we continue to consider the usefulness of the new DSCSI metric for evaluating quantized images. Our use of the DSCSI metric confirmed that the color quantization in the CIELAB color space is better than in the basic RGB color space. On several examples we found very good DSCSI suitability in the case of quantization with dithering. During the tests of different dithering algorithms the best results, in terms of DSCSI metric, reached the classical Floyd-Steinberg algorithm at error propagation level of 75-85%.

Bulletin of The Polish Academy of Sciences-technical Sciences, 2018

Ultrasonography has proved its usefulness in the evaluation of joint inflammations caused by rheu... more Ultrasonography has proved its usefulness in the evaluation of joint inflammations caused by rheumatoid arthritis. The illness severity is scored by human examiners based on their experience, but some discrepancies in the final diagnosis and treatment frequently occur. Therefore, the main aim of this work is the elaboration of an automatic method of the localization of finger joint inflammation level in ultrasound images. In this paper we propose a novel, fully automated framework for synovitis region segmentation. In our approach we compare several bones and joint localization methods based on the seeded region growing technique, which is combined with different speckle noise filtering algorithms. This technique extracts a region from the image using some predefined criteria of similarity between initially selected point and the pixels in its neighborhood. The seed point is localized automatically as the darkest patch within a small region between two detected finger bones close to the joint. The region affected by synovitis is found using the adopted criterion of homogeneity based on a patch to patch similarity measure. The obtained results exhibit a satisfying accuracy in comparison with the annotations prepared by an expert and the results delivered by semi-automatic methods that require manual bones delineation.

Symmetry, Aug 1, 2019

Color image quantization has become an important operation often used in tasks of color image pro... more Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu's algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.

Proceedings of SPIE, Mar 17, 2017

Color quantization is an important operation in the field of color image processing. In this pape... more Color quantization is an important operation in the field of color image processing. In this paper, we consider a usefulness of the new DSCSI metric to assessment of quantized images. This metric is shown in the background of other useful image quality metrics to evaluate the color image differences and it has also been proven that DSCSI metric achieves the highest correlation coefficients with MOS. For further veriffcation DSCSI metric the combined methods that use to initialize the results of well-known splitting algorithms such as POP, MC, Wu etc. were tested. Experimental results of such combined methods indicate that the Wu+KM approach leading to the best quantized images in the sense of DSCSI metric.

Pomiary Automatyka Kontrola, 2007

Breast cancer is one of the most common cancers in women. Typically, the course of the disease is... more Breast cancer is one of the most common cancers in women. Typically, the course of the disease is asymptomatic in the early stages of breast cancer. Imaging breast examinations allow early detection of the cancer, which is associated with increased chances of a complete cure. There are many breast imaging techniques such as: mammography (MM), ultrasound imaging (US), positron-emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI). These imaging techniques differ in terms of effectiveness, price, type of physical phenomenon, the impact on the patient and its availability. In this paper, we focus on MRI imaging and we compare three breast lesion segmentation algorithms that have been tested on QIN Breast DCE-MRI database, which is publicly available. The obtained values of Dice and Jaccard indices indicate the segmentation using k-means algorithm.

Sensors, Jan 18, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Sensors, Aug 12, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The aim of this work was to test the most popular and essential algorithms of the intensity nonun... more The aim of this work was to test the most popular and essential algorithms of the intensity nonuniformity correction of the breast MRI imaging. In this type of MRI imaging, especially in the proximity of the coil, the signal is strong but also can produce some inhomogeneities. Evaluated methods of signal correction were: N3, N3FCM, N4, Nonparametric, and SPM. For testing purposes, a uniform phantom object was used to obtain test images using breast imaging MRI coil. To quantify the results, two measures were used: integral uniformity and standard deviation. For each algorithm minimum, average and maximum values of both evaluation factors have been calculated using the binary mask created for the phantom. In the result, two methods obtained the lowest values in these measures: N3FCM and N4, however, for the second method visually phantom was the most uniform after correction.

The main goal of colour quantization methods is a colour reduction with minimum colour error. In ... more The main goal of colour quantization methods is a colour reduction with minimum colour error. In this paper were investigated six following colour quantization techniques: the classical median cut, improved median cut, clustering k-means technique in two colour versions (RGB, CIELAB) and also two versions of relative novel technique named k-harmonic means. The comparison presented here was based on testing of ten natural colour images for quantization into 16, 64 and 256 colours. In evaluation process two criteria were used: the mean squared quantization error (MSE) and the average error in the CIELAB colour space (DeltaE). During tests the efficiency of k-harmonic means applied to colour quantization has been proved.