Pub Date : 2012-10-01DOI: 10.1109/IPTA.2012.6469514
T. Economopoulos, P. Asvestas, G. Matsopoulos
A new technique is presented for enhancing the contrast of digital images. The proposed method is based on the regional application of the Partitioned Iterated Function Systems (PIFS) algorithm. The subject image is partitioned into domain regions, using a standard Region Growing approach. Each domain region is further partitioned into smaller range regions. In turn, each range region is transformed through a contractive affine spatial transform, as well as through a linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image, after processing each region. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The quantitative and qualitative results obtained, show that the proposed method achieves higher quality image enhancement, compared to two widely used contrast enhancement techniques.
{"title":"Regional Partitioned Iterated Function Systems for digital image enhancement","authors":"T. Economopoulos, P. Asvestas, G. Matsopoulos","doi":"10.1109/IPTA.2012.6469514","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469514","url":null,"abstract":"A new technique is presented for enhancing the contrast of digital images. The proposed method is based on the regional application of the Partitioned Iterated Function Systems (PIFS) algorithm. The subject image is partitioned into domain regions, using a standard Region Growing approach. Each domain region is further partitioned into smaller range regions. In turn, each range region is transformed through a contractive affine spatial transform, as well as through a linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image, after processing each region. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The quantitative and qualitative results obtained, show that the proposed method achieves higher quality image enhancement, compared to two widely used contrast enhancement techniques.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116015809","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469517
B. T. Widemann, C. Bogner
Flow processes in soils are closely related to groundwater quality often affected by human activities. Because hydrological models usually lack explanatory power, direct visualization of flow paths in dye tracer infiltration studies has become a standard tool in soil hydrology. These experiments provide images of dye-stained paths in soils and help evaluating the vulnerability or understanding the general hydrological functioning of a given site. Extracting relevant information demands expertise in hydrology as well as in image analysis and statistics. To our knowledge, no agreed and effective method to analyze large collections of such images exists in the soil hydrology community. In this paper we propose a general framework consisting of index functions and visual tools to support the expert in his/her evaluation of dye tracer infiltration images.
{"title":"Image analysis for soil dye tracer infiltration studies","authors":"B. T. Widemann, C. Bogner","doi":"10.1109/IPTA.2012.6469517","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469517","url":null,"abstract":"Flow processes in soils are closely related to groundwater quality often affected by human activities. Because hydrological models usually lack explanatory power, direct visualization of flow paths in dye tracer infiltration studies has become a standard tool in soil hydrology. These experiments provide images of dye-stained paths in soils and help evaluating the vulnerability or understanding the general hydrological functioning of a given site. Extracting relevant information demands expertise in hydrology as well as in image analysis and statistics. To our knowledge, no agreed and effective method to analyze large collections of such images exists in the soil hydrology community. In this paper we propose a general framework consisting of index functions and visual tools to support the expert in his/her evaluation of dye tracer infiltration images.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128931598","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469521
A. Derbel, Y. Jemaa, R. Canals, B. Emile, S. Treuillet, A. B. Hamadou
In this paper, we propose a comparative study between different descriptors based on color, texture and shape information. In particular, our study is focused on measuring the robustness of these descriptors in terms of identifing a person in a camera network. We prove through experimental study based on VIPeR pedestrians images dataset and “Cumulative Matching Characteristic” (CMC) measurement that color descriptors are the most appropriate in multi-camera context: they are less sensitive to the highly articulated human body, changes in lighting conditions and large pose variations.
{"title":"Comparative study between color texture and shape descriptors for multi-camera pedestrians identification","authors":"A. Derbel, Y. Jemaa, R. Canals, B. Emile, S. Treuillet, A. B. Hamadou","doi":"10.1109/IPTA.2012.6469521","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469521","url":null,"abstract":"In this paper, we propose a comparative study between different descriptors based on color, texture and shape information. In particular, our study is focused on measuring the robustness of these descriptors in terms of identifing a person in a camera network. We prove through experimental study based on VIPeR pedestrians images dataset and “Cumulative Matching Characteristic” (CMC) measurement that color descriptors are the most appropriate in multi-camera context: they are less sensitive to the highly articulated human body, changes in lighting conditions and large pose variations.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116479410","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469510
S. Oudjemia, J. Girault, S. Haddab, A. Ouahabi, Z. Ameur
We show the relevance of multifractal analysis for some problems in image. This paper deals the characterization of brain tumor in magnetic resonance imaging. We introduce a declination of wavelet Leaders that recently been shown to provide practioners with a robust and efficient tool for the multifractal analysis of signals and images. We calculated new multiresolution parameters called average of wavelet coefficient and the log-cumulate derived from the wavelet leaders and we have solved the problem posed by the choice of interval regression that enters in the calculation of different parameters (h(q), D(q), ζ(q)). We analyze and compare our estimator and simulated image against wavelet leaders. We apply the approach developed on different cerebral images in order to distinguish between different tissues corresponding to the healthy and pathological.
{"title":"Multifractal analysis based on discrete wavelet for texture classification: Application to medical magnetic resonance imaging","authors":"S. Oudjemia, J. Girault, S. Haddab, A. Ouahabi, Z. Ameur","doi":"10.1109/IPTA.2012.6469510","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469510","url":null,"abstract":"We show the relevance of multifractal analysis for some problems in image. This paper deals the characterization of brain tumor in magnetic resonance imaging. We introduce a declination of wavelet Leaders that recently been shown to provide practioners with a robust and efficient tool for the multifractal analysis of signals and images. We calculated new multiresolution parameters called average of wavelet coefficient and the log-cumulate derived from the wavelet leaders and we have solved the problem posed by the choice of interval regression that enters in the calculation of different parameters (h(q), D(q), ζ(q)). We analyze and compare our estimator and simulated image against wavelet leaders. We apply the approach developed on different cerebral images in order to distinguish between different tissues corresponding to the healthy and pathological.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121682997","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469545
Amir Benzaoui, H. Bourouba, A. Boukrouche
The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image or video. This paper presents a hybrid system for faces detection, in a color image or video, in unconstrained cases, i.e. situations in which illumination, pose, occlusion and size of the face are uncontrolled. To do this, the new method of detection proposed in this system is based primarily on a technique of automatic learning by using the decision of three neural networks, a new method of feature extraction based on the principal of energy compaction in the DC coefficient using the discrete cosine transform and a technique of segmentation by skin color to reduce the space of research and to accelerate the process of detection. A whole of pictures (faces and no faces) are transformed to vectors of data which will be used for entrain the neural networks to separate between the two classes while the discrete cosine transform is used to reduce the dimension of the vectors, to eliminate the redundancies of information, and to store only the useful information in a minimum number of coefficients. The experimental results have showed that this hybridization of methods will gave a very significant improvement of the rate of the recognition, quality of detection and the time of execution.
{"title":"System for automatic faces detection","authors":"Amir Benzaoui, H. Bourouba, A. Boukrouche","doi":"10.1109/IPTA.2012.6469545","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469545","url":null,"abstract":"The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image or video. This paper presents a hybrid system for faces detection, in a color image or video, in unconstrained cases, i.e. situations in which illumination, pose, occlusion and size of the face are uncontrolled. To do this, the new method of detection proposed in this system is based primarily on a technique of automatic learning by using the decision of three neural networks, a new method of feature extraction based on the principal of energy compaction in the DC coefficient using the discrete cosine transform and a technique of segmentation by skin color to reduce the space of research and to accelerate the process of detection. A whole of pictures (faces and no faces) are transformed to vectors of data which will be used for entrain the neural networks to separate between the two classes while the discrete cosine transform is used to reduce the dimension of the vectors, to eliminate the redundancies of information, and to store only the useful information in a minimum number of coefficients. The experimental results have showed that this hybridization of methods will gave a very significant improvement of the rate of the recognition, quality of detection and the time of execution.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115954758","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469557
Sabin Tiberius Strat, A. Benoît, P. Lambert, A. Caplier
This paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static images to video sequences exhibits some new problems to cope with, mainly the way to use the added temporal dimension for detecting the target concepts (swimming, drinking...). In this study, we propose to apply a human retina model to preprocess video sequences, before constructing a State-Of-The-Art BoW analysis. This preprocessing, designed in a way that enhances the appearance especially of static image elements, increases the performance by introducing robustness to traditional image and video problems, such as luminance variation, shadows, compression artifacts and noise. These approaches are evaluated on the TrecVid 2010 Semantic Indexing task datasets, containing 130 high-level semantic concepts. We consider the well-known SURF descriptor as the entry point of the BoW system, but this work could be extended to any other local gradient based descriptor.
{"title":"Retina-enhanced SURF descriptors for semantic concept detection in videos","authors":"Sabin Tiberius Strat, A. Benoît, P. Lambert, A. Caplier","doi":"10.1109/IPTA.2012.6469557","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469557","url":null,"abstract":"This paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static images to video sequences exhibits some new problems to cope with, mainly the way to use the added temporal dimension for detecting the target concepts (swimming, drinking...). In this study, we propose to apply a human retina model to preprocess video sequences, before constructing a State-Of-The-Art BoW analysis. This preprocessing, designed in a way that enhances the appearance especially of static image elements, increases the performance by introducing robustness to traditional image and video problems, such as luminance variation, shadows, compression artifacts and noise. These approaches are evaluated on the TrecVid 2010 Semantic Indexing task datasets, containing 130 high-level semantic concepts. We consider the well-known SURF descriptor as the entry point of the BoW system, but this work could be extended to any other local gradient based descriptor.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802075","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469576
Y. K. Al-Audah, A. Al-Juraifani, Mohamed Deriche
License Plate Recognition (LPR) systems play an important role in intelligent transportation applications. These systems have been extensively used in highway and bridge charge, ports, airports, gate monitoring, parking and toll applications, to mention a few. In this paper, we propose a real-time LPR system that uses the LabVIEW software and is based on the NI-Camera Vision System. The system adapts automatically to detect license plates from the GCC countries and identify both English & Arabic letters and Numerals. The system uses recently introduced LPs in Saudi Arabia to test the real-time operation. The system was tested using low-resolution images of just 640×480 pixels and achieved a success rate of 94% under optimal conditions. Most importantly, the processing time is under 40 ms/plate outperforming most existing systems which average around 100 ms/plate.
{"title":"A real-time license plate recognition system for Saudi Arabia using LabVIEW","authors":"Y. K. Al-Audah, A. Al-Juraifani, Mohamed Deriche","doi":"10.1109/IPTA.2012.6469576","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469576","url":null,"abstract":"License Plate Recognition (LPR) systems play an important role in intelligent transportation applications. These systems have been extensively used in highway and bridge charge, ports, airports, gate monitoring, parking and toll applications, to mention a few. In this paper, we propose a real-time LPR system that uses the LabVIEW software and is based on the NI-Camera Vision System. The system adapts automatically to detect license plates from the GCC countries and identify both English & Arabic letters and Numerals. The system uses recently introduced LPs in Saudi Arabia to test the real-time operation. The system was tested using low-resolution images of just 640×480 pixels and achieved a success rate of 94% under optimal conditions. Most importantly, the processing time is under 40 ms/plate outperforming most existing systems which average around 100 ms/plate.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127484836","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469546
L. Houam, A. Hafiane, A. Boukrouche, E. Lespessailles, R. Jennane
In this paper, we propose a method based on wavelet coefficients associated with 2D and 1D Local Binary Pattern (LBP) descriptors to classify X-ray bone images for bone disease diagnosis. The proposed approach uses two types of algorithms: the “À trous” algorithm that uses B3-spline as a wavelet basis function and the “Mallat” algorithm with the Daubechie wavelet function. The wavelet decomposition is applied to the 2D image and to its projection. Then, the LBP descriptors are performed in both cases. Two approaches were adopted, the first one compares the LBP histograms and the second derives statistical measures from the histograms to form different feature vectors. Experiments were conducted on two populations of osteoporotic patients and control subjects. Results show that the 1D projected field of the 2D images achieves better results for the classification of the two populations.
{"title":"Texture characterization using local binary pattern and wavelets. Application to bone radiographs","authors":"L. Houam, A. Hafiane, A. Boukrouche, E. Lespessailles, R. Jennane","doi":"10.1109/IPTA.2012.6469546","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469546","url":null,"abstract":"In this paper, we propose a method based on wavelet coefficients associated with 2D and 1D Local Binary Pattern (LBP) descriptors to classify X-ray bone images for bone disease diagnosis. The proposed approach uses two types of algorithms: the “À trous” algorithm that uses B3-spline as a wavelet basis function and the “Mallat” algorithm with the Daubechie wavelet function. The wavelet decomposition is applied to the 2D image and to its projection. Then, the LBP descriptors are performed in both cases. Two approaches were adopted, the first one compares the LBP histograms and the second derives statistical measures from the histograms to form different feature vectors. Experiments were conducted on two populations of osteoporotic patients and control subjects. Results show that the 1D projected field of the 2D images achieves better results for the classification of the two populations.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127487858","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469533
Huu-Tuan Nguyen, Ngoc-Son Vu, A. Caplier
This paper presents improvements for face recognition methods that use LBP descriptor as a main technique in encoding micro features of face images. Our improvements are focused on the feature extraction and dimension reduction steps. In feature extraction, we use a variant of Local Binary Pattern (LBP) so-called Elliptical Local Binary Pattern (ELBP), which is more efficient than LBP for extracting micro facial features of the human face. ELBP of one pixel is built by thresholding its gray value with its P neighboring pixels on a horizontal ellipse. ELBP operator is applied in Pattern of Oriented Edge Magnitudes (POEM) to build Elliptical POEM (EPOEM) descriptor. The dimension reduction step is conducted by using Singular Value Decomposition (SVD) based Whitened Principal Component Analysis (WPCA). For performance evaluation of our improvements, we compare them with LBP based, POEM based approaches and other popular face recognition systems. The experimental results on state-of-the-art FERET and AR face databases prove the advantages and effectiveness of our improvements.
{"title":"How far we can improve micro features based face recognition systems?","authors":"Huu-Tuan Nguyen, Ngoc-Son Vu, A. Caplier","doi":"10.1109/IPTA.2012.6469533","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469533","url":null,"abstract":"This paper presents improvements for face recognition methods that use LBP descriptor as a main technique in encoding micro features of face images. Our improvements are focused on the feature extraction and dimension reduction steps. In feature extraction, we use a variant of Local Binary Pattern (LBP) so-called Elliptical Local Binary Pattern (ELBP), which is more efficient than LBP for extracting micro facial features of the human face. ELBP of one pixel is built by thresholding its gray value with its P neighboring pixels on a horizontal ellipse. ELBP operator is applied in Pattern of Oriented Edge Magnitudes (POEM) to build Elliptical POEM (EPOEM) descriptor. The dimension reduction step is conducted by using Singular Value Decomposition (SVD) based Whitened Principal Component Analysis (WPCA). For performance evaluation of our improvements, we compare them with LBP based, POEM based approaches and other popular face recognition systems. The experimental results on state-of-the-art FERET and AR face databases prove the advantages and effectiveness of our improvements.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125492148","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 : 2012-10-01DOI: 10.1109/IPTA.2012.6469558
Ertunç Erdil, A. M. Yagci, Ali Ozgur Argunsah, Y. Ramiro-Cortes, A. F. Hobbiss, Inbal Israely, D. Ünay
We propose an image processing pipeline for dendritic spine detection in two-photon fluorescence microscopy images. Spines of interest to neuroscientists often contain high intensity regions with respect to their surroundings. We find such maxima regions using morphological image reconstruction. These regions facilitate a multi-level segmentation algorithm to detect spines. First, watershed algorithm is applied to extract initial rough regions of spines. Then, these results are further refined using a graph-theoretic region-growing algorithm which incorporates segmentation on a sparse representation of image data and hierarchical clustering as a post-processing step. We compare our final results to segmentation results of the domain expert. Our pipeline produces promising segmentation results with practical run times for monitoring streaming data.
{"title":"A tool for automatic dendritic spine detection and analysis. Part I: Dendritic spine detection using multi-level region-based segmentation","authors":"Ertunç Erdil, A. M. Yagci, Ali Ozgur Argunsah, Y. Ramiro-Cortes, A. F. Hobbiss, Inbal Israely, D. Ünay","doi":"10.1109/IPTA.2012.6469558","DOIUrl":"https://doi.org/10.1109/IPTA.2012.6469558","url":null,"abstract":"We propose an image processing pipeline for dendritic spine detection in two-photon fluorescence microscopy images. Spines of interest to neuroscientists often contain high intensity regions with respect to their surroundings. We find such maxima regions using morphological image reconstruction. These regions facilitate a multi-level segmentation algorithm to detect spines. First, watershed algorithm is applied to extract initial rough regions of spines. Then, these results are further refined using a graph-theoretic region-growing algorithm which incorporates segmentation on a sparse representation of image data and hierarchical clustering as a post-processing step. We compare our final results to segmentation results of the domain expert. Our pipeline produces promising segmentation results with practical run times for monitoring streaming data.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127151502","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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