Pub Date : 2020-08-31DOI: 10.5121/sipij.2020.11401
Salah Alheejawi, R. Berendt, N. Jha, M. Mandal
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.
{"title":"Melanoma Cell Detection in Lymph Nodes Histopathological Images using Deep Learning","authors":"Salah Alheejawi, R. Berendt, N. Jha, M. Mandal","doi":"10.5121/sipij.2020.11401","DOIUrl":"https://doi.org/10.5121/sipij.2020.11401","url":null,"abstract":"Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"1 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88769045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-31DOI: 10.5121/sipij.2020.11403
C. Kwan, Jude Larkin, Bence Budavari
In CFA 2.0, there are white pixels in a color filter array (CFA) that has proven to help the demosaicing performance for images collected in low light conditions. Here, we evaluate the performance of demosaicing for images collected in low light conditions using an RGBW pattern with 75% white pixels. We term this CFA the CFA 3.0. Using a data set containing 10 images collected in low light conditions, we performed extensive experiments. Both objective and subjective evaluations were used in our experiments.
{"title":"Demosaicing of Real Low Lighting Images using CFA 3.0","authors":"C. Kwan, Jude Larkin, Bence Budavari","doi":"10.5121/sipij.2020.11403","DOIUrl":"https://doi.org/10.5121/sipij.2020.11403","url":null,"abstract":"In CFA 2.0, there are white pixels in a color filter array (CFA) that has proven to help the demosaicing performance for images collected in low light conditions. Here, we evaluate the performance of demosaicing for images collected in low light conditions using an RGBW pattern with 75% white pixels. We term this CFA the CFA 3.0. Using a data set containing 10 images collected in low light conditions, we performed extensive experiments. Both objective and subjective evaluations were used in our experiments.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"23 1","pages":"25-41"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72998112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-14DOI: 10.21203/rs.3.rs-47495/v1
Marwan Aldahami, Umar S. Alqasemi
Background Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease because of their capability of capturing micrometer-resolution.Method An automated technique was introduced to differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160 images were used for classifiers’ training, and 54 images were used for testing. Different features were extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features.Results The experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal retina with ROC Area Under the Curve (AUC) of 100%.Conclusions The retinal OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP features has a significant impact on the achieved results. The result has better performance than previously proposed methods in the literature.
{"title":"Classification of OCT Images for Detecting Diabetic Retinopathy Disease Using Machine Learning","authors":"Marwan Aldahami, Umar S. Alqasemi","doi":"10.21203/rs.3.rs-47495/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-47495/v1","url":null,"abstract":"\u0000 Background Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease because of their capability of capturing micrometer-resolution.Method An automated technique was introduced to differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160 images were used for classifiers’ training, and 54 images were used for testing. Different features were extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features.Results The experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal retina with ROC Area Under the Curve (AUC) of 100%.Conclusions The retinal OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP features has a significant impact on the achieved results. The result has better performance than previously proposed methods in the literature.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77100578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-30DOI: 10.5121/sipij.2020.11201
C. Kwan, Jude Larkin
In video compression class projects, students may observe some strange behaviors when using video codecs. Some performance metrics from a mediocre codec such as motion JPEG-2000 (or simply JPEG2000) may have exceptionally high values at certain compression ratios as compared to other high performing codecs. This strange behaviors may be overlooked by instructors and students may never understand why this is happening. In this paper, we will first highlight the strange behaviors. We will then use experiments to systematically determine the root cause. Our experiments show that, if one uses a previously compressed and decompressed video in some compression experiments, then it is highly likely that some strange behaviors will show up. Some advice will be provided to instructors, tutors, and students on how one can prevent such behaviors from occurring.
{"title":"Strange Behaviors and Root Cause in the Compression of Previously Compressed Videos","authors":"C. Kwan, Jude Larkin","doi":"10.5121/sipij.2020.11201","DOIUrl":"https://doi.org/10.5121/sipij.2020.11201","url":null,"abstract":"In video compression class projects, students may observe some strange behaviors when using video codecs. Some performance metrics from a mediocre codec such as motion JPEG-2000 (or simply JPEG2000) may have exceptionally high values at certain compression ratios as compared to other high performing codecs. This strange behaviors may be overlooked by instructors and students may never understand why this is happening. In this paper, we will first highlight the strange behaviors. We will then use experiments to systematically determine the root cause. Our experiments show that, if one uses a previously compressed and decompressed video in some compression experiments, then it is highly likely that some strange behaviors will show up. Some advice will be provided to instructors, tutors, and students on how one can prevent such behaviors from occurring.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"139 1","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75743118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-29DOI: 10.5121/sipij.2020.11104
Romain Atangana, D. Tchiotsop, G. Kenné, Laurent Chanel Djoufack Nkengfack
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
{"title":"Suitable Mother Wavelet Selection for EEG Signals Analysis: Frequency Bands Decomposition and Discriminative Feature Selection","authors":"Romain Atangana, D. Tchiotsop, G. Kenné, Laurent Chanel Djoufack Nkengfack","doi":"10.5121/sipij.2020.11104","DOIUrl":"https://doi.org/10.5121/sipij.2020.11104","url":null,"abstract":"Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"260 1","pages":"33-49"},"PeriodicalIF":0.0,"publicationDate":"2020-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79616151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-29DOI: 10.5121/sipij.2020.11103
Umar Alqasmi, Ammar Alzuhair, Abduallah Bama'bad
{"title":"Enhanced System for Computer-aided Detection of MRI Brain Tumors","authors":"Umar Alqasmi, Ammar Alzuhair, Abduallah Bama'bad","doi":"10.5121/sipij.2020.11103","DOIUrl":"https://doi.org/10.5121/sipij.2020.11103","url":null,"abstract":"","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"29 1","pages":"25-31"},"PeriodicalIF":0.0,"publicationDate":"2020-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75068932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-29DOI: 10.5121/sipij.2020.11102
R. Sabre, I. Wahyuni
This article gives a new method of fusing multifocal images combining the Laplacian pyramid and the wavelet decomposition using the stable distance alpha as a selection rule. We start by decomposing multifocal images into several pyramid levels, then applying the wavelet decomposition to each level. the originality of this work is to use the stable distance alpha to fuse the wavelet images at each level of the Pyramid. To obtain the final fused image, we reconstructed the combined image at each level of the pyramid. We compare our method to other existing methods in the literature and we deduce that it is almost better.
{"title":"Wavelet Decomposition and Alpha Stable Fusion","authors":"R. Sabre, I. Wahyuni","doi":"10.5121/sipij.2020.11102","DOIUrl":"https://doi.org/10.5121/sipij.2020.11102","url":null,"abstract":"This article gives a new method of fusing multifocal images combining the Laplacian pyramid and the wavelet decomposition using the stable distance alpha as a selection rule. We start by decomposing multifocal images into several pyramid levels, then applying the wavelet decomposition to each level. the originality of this work is to use the stable distance alpha to fuse the wavelet images at each level of the Pyramid. To obtain the final fused image, we reconstructed the combined image at each level of the pyramid. We compare our method to other existing methods in the literature and we deduce that it is almost better.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"284 1","pages":"11-24"},"PeriodicalIF":0.0,"publicationDate":"2020-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76614887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-31DOI: 10.5121/sipij.2019.10602
Roxana Flores-Quispe, Yuber Velazco-Paredes
This paper proposes a method based on Multitexton Histogram (MTH) descriptor to identify patterns inimages of human parasite eggs of the following species: Ascaris, Uncinarias, Trichuris, Hymenolepis Nana, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepatica and Enterobius-Vermicularis. These patterns are represented by textons of irregular shapes in their microscopic images. This proposed method could be used for diagnosis of Parasitic disease and it can be helpful especially in remote places. This paper includes two stages. In the first a feature extraction mechanism integrates the advantages of cooccurrence matrix and histograms to identify irregular morphological structures in the biological images through textons of irregular shape. In the second stage the Support Vector Machine (SVM) is used to classificate the different human parasite eggs. The results were obtaining using a dataset with 2053 human parasite eggs images achieving a success rate of 96,82% in the classification. In addition, this research shows that the proposed method also works with natural images.
{"title":"Deep Learning Based Target Tracking and Classification Directly in Compressive Measurement for Low Quality Videos","authors":"Roxana Flores-Quispe, Yuber Velazco-Paredes","doi":"10.5121/sipij.2019.10602","DOIUrl":"https://doi.org/10.5121/sipij.2019.10602","url":null,"abstract":"This paper proposes a method based on Multitexton Histogram (MTH) descriptor to identify patterns inimages of human parasite eggs of the following species: Ascaris, Uncinarias, Trichuris, Hymenolepis Nana, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepatica and Enterobius-Vermicularis. These patterns are represented by textons of irregular shapes in their microscopic images. This proposed method could be used for diagnosis of Parasitic disease and it can be helpful especially in remote places. This paper includes two stages. In the first a feature extraction mechanism integrates the advantages of cooccurrence matrix and histograms to identify irregular morphological structures in the biological images through textons of irregular shape. In the second stage the Support Vector Machine (SVM) is used to classificate the different human parasite eggs. The results were obtaining using a dataset with 2053 human parasite eggs images achieving a success rate of 96,82% in the classification. In addition, this research shows that the proposed method also works with natural images.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90122001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-31DOI: 10.5121/sipij.2019.10601
Omar Y. Adwan
Flocking is a behaviour in which objects move or work together as a group. This behaviour is very common in nature think of a flock of flying geese or a school of fish in the sea. Flocking behaviours have been simulated in different areas such as computer animation, graphics and games. However, the simulation of the flocking behaviours of large number of objects in real time is computationally intensive task. This intensity is due to the n-squared complexity of the nearest neighbour (NN) algorithm used to separate objects, where n is the number of objects. This paper proposes an efficient NN method based on the partial distance approach to enhance the performance of the flocking algorithm and its application to flocking behaviour. The proposed method was implemented and the experimental results showed that the proposed method outperformed conventional NN methods when applied to flocking fish.
{"title":"Efficient Method to find Nearest Neighbours in Flocking Behaviours","authors":"Omar Y. Adwan","doi":"10.5121/sipij.2019.10601","DOIUrl":"https://doi.org/10.5121/sipij.2019.10601","url":null,"abstract":"Flocking is a behaviour in which objects move or work together as a group. This behaviour is very common in nature think of a flock of flying geese or a school of fish in the sea. Flocking behaviours have been simulated in different areas such as computer animation, graphics and games. However, the simulation of the flocking behaviours of large number of objects in real time is computationally intensive task. This intensity is due to the n-squared complexity of the nearest neighbour (NN) algorithm used to separate objects, where n is the number of objects. This paper proposes an efficient NN method based on the partial distance approach to enhance the performance of the flocking algorithm and its application to flocking behaviour. The proposed method was implemented and the experimental results showed that the proposed method outperformed conventional NN methods when applied to flocking fish.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"55 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78812590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-31DOI: 10.5121/sipij.2019.10603
Roxana Flores-Quispe, Yuber Velazco-Paredes
This paper proposes a method based on Multitexton Histogram (MTH) descriptor to identify patterns in images of human parasite eggs of the following species: Ascaris, Uncinarias, Trichuris, Hymenolepis Nana, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepática and Enterobius-Vermicularis. These patterns are represented by textons of irregular shapes in their microscopic images. This proposed method could be used for diagnosis of Parasitic disease and it can be helpful especially in remote places. This paper includes two stages. In the first a feature extraction mechanism integrates the advantages of cooccurrence matrix and histograms to identify irregular morphological structures in the biological images through textons of irregular shape. In the second stage the Support Vector Machine (SVM) is used to classificate the different human parasite eggs. The results were obtaining using a dataset with 2053 human parasite eggs images achieving a success rate of 96,82% in the classification. In addition, this research shows that the proposed method also works with natural images.
{"title":"Textons of Irregular Shape to Identify Patterns in the Human Parasite Eggs","authors":"Roxana Flores-Quispe, Yuber Velazco-Paredes","doi":"10.5121/sipij.2019.10603","DOIUrl":"https://doi.org/10.5121/sipij.2019.10603","url":null,"abstract":"This paper proposes a method based on Multitexton Histogram (MTH) descriptor to identify patterns in images of human parasite eggs of the following species: Ascaris, Uncinarias, Trichuris, Hymenolepis Nana, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepática and Enterobius-Vermicularis. These patterns are represented by textons of irregular shapes in their microscopic images. This proposed method could be used for diagnosis of Parasitic disease and it can be helpful especially in remote places. This paper includes two stages. In the first a feature extraction mechanism integrates the advantages of cooccurrence matrix and histograms to identify irregular morphological structures in the biological images through textons of irregular shape. In the second stage the Support Vector Machine (SVM) is used to classificate the different human parasite eggs. The results were obtaining using a dataset with 2053 human parasite eggs images achieving a success rate of 96,82% in the classification. In addition, this research shows that the proposed method also works with natural images.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87230116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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