99 research outputs found
The CAPA Apple Quality Grading Multi-Spectral Image Database
No full text<p>The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc. The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection.</p>
<p>The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium. <br>
For consent, contact<br>
Devrim Unay, İzmir University of Economics, Turkey: [email protected]<br>
OR<br>
Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: [email protected]</p>
<p><br>
In disseminating results using this database, <br>
1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium.<br>
2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49.</p>
<p>Relevant publications:<br>
Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol., 30 (2003), pp. 221–232.<br>
Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng., 61 (2004), pp. 83–89.<br>
Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng., 83 (2002), pp. 397–404.<br>
Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol., 42 (2006), pp. 271–279.<br>
Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng., 78 (2007), pp. 597–605.<br>
Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.<br>
</p>
Effects of Covariates on Classification of Bipolar Disorder Using Structural MRI
No full textInternational Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEYOguz, Kaya/0000-0002-1860-9127; Unay, Devrim/0000-0003-3478-7318Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection and diagnosis of neuroanatomical abnormalities, including bipolar disorder (BD). Pre-processing of 3D-MRI scans plays an important role in post-processing. in this study, Voxel-Based Morphometry (VBM) is used to compare the morphological differences at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs). the effects of using different covariates (i.e. total intracranial volume (TIV), age, sex, and their combinations) on classification of BDs from HCs have been investigated for GM-only, WM-only, and their combination. 3D masks for GM and WM are generated separately by using local differences between BPs and HCs and the two sample t-test method. Principle component analysis based dimensionality reduction and support vector machine with Gaussian kernel are employed for classification of 26 BDs and 38 HCs obtained from Ege University, School of Medicine, Department of Psychiatry. the results indicate that using only TIV as a covariate provides more robust results for BD classification compared to other covariate combinations. Furthermore, the combination of GM and WM improves classification performance. the highest classification accuracies obtained for GM, WM, and their combination are 70.30%, 79.70%, and 82.80% respectively.IEEE Turkey Sect, IEEE EMB, Erasmus+, Europas
Augmenting clinical observations with visual features from longitudinal MRI data for improved dementia diagnosis
No full textMultispectral image processing and pattern recognition techniques for quality inspection of apple fruits
No full textQuality inspection of apple fruits, traditionally performed by human experts, has to be automated by machine vision to reduce error, variation, fatigue and cost due to humans as well as to increase speed…
A typical apple inspection system should employ image processing and pattern recognition techniques to precisely segment defected skin by minimal confusion with stem/calyx areas and classify fruit into correct quality category. In this thesis, we present a work performed for quality inspection of bi-colored apples using multispectral images by tackling each of these sub-problems (namely, stem/calyx recognition, defect detection and fruit grading) individually. Stem and calyx are natural parts of apples that are confused with some defects in machine vision systems. A precise inspection system requires their discrimination, which is achieved by a highly accurate support vector machines-based approach. Defect detection of apples by machine vision is very problematic due to numerous defect types present as well as high natural variability of skin color. This task is accomplished by multi-layer perceptrons (an artificial neural network), which outperformed several other methods in accuracy and speed. Final grading of fruit is obtained by binary and multi-category classification with different classifiers, where results achieved are very encouraging.DOCSCAPPL - Doctorat en Sciences appliquée
Dual-infinite coordination polymer-engineered nanomedicines for dual-ion interference-mediated oxidative stress-dependent tumor suppression
No full textRecently, nanomedicine design has shifted from simple nanocarriers to nanodrugs with intrinsic antineoplastic activities for therapeutic performance optimization. In this regard, degradable nanomedicines containing functional inorganic ions have blazed a highly efficient and relatively safe ion interference paradigm for cancer theranostics. Herein, given the potential superiorities of infinite coordination polymers (ICPs) in degradation peculiarity and functional integration, a state-of-the-art dual-ICP-engineered nanomedicine is elaborately fabricated via integrating ferrocene (Fc) ICPs and calcium-tannic acid (Ca-TA) ICPs. Thereinto, Fc ICPs, and Ca-TA ICPs respectively serve as suppliers of ferrous iron ions (Fe2+) and calcium ions (Ca2+). After the acid-responsive degradation of ICPs, released TA from Ca-TA ICPs facilitated the conversion of released ferric iron (Fe3+) from Fc ICPs into highly active Fe2+. Owing to the dual-path oxidative stress and neighboring effect mediated by Fe2+ and Ca2+, such a dual-ICP-engineered nanomedicine effectively induces dual-ion interference against triple-negative breast cancer (TNBC). Therefore, this work provides a novel antineoplastic attempt to establish ICP-engineered nanomedicines and implement ion interference-mediated synergistic therapy.
Retrieval from and understanding of large–scale multi–modal medical datasets ::a review
No full textContent–based multimedia retrieval has been an active research domain since the mid 1990s. In the medical domain visual retrieval started later and has mostly remained a research instrument and less a clinical tool, even though a few tools for retrieval are employed in clinical work. The limited size of data sets due to privacy constraints is often mentioned as a reason for these limitations. Nevertheless, much work has been done in medical visual information retrieval, including the availability of increasingly large data sets and scientific challenges. Annotated data sets and clinical data for the images have now become available and can be combined for multi– modal retrieval. Much has been learned on user behavior and application scenarios. This text is motivated by the advances in medical image analysis and the availability of more public data large data sets that often include clinical data that can be combined for multimodal retrieval based on the experience available in the multimedia community. This text is a systematic review of recent work (concentrating on the period between 2011-2017) on content–based multi–modal retrieval and image understanding in the medical domain, where image understanding includes techniques such as detection, localization, and classification for leveraging visual content. The main conferences in the field are screened for relevant articles and these are presented in a structured way, identifying current limitations and areas where work is still much required. Objective of the work is to summarize the current state of research for multimedia researchers not working in the medical field. It provides ways to get data sets and identify promising research directions. The text highlights the areas of advances in the past six years and particularly a trend to use larger scale training data sets as well as deep learning approaches that can replace or complement hand–crafted feature extraction. Using images alone will likely only work in limited sub domains but combining multiple sources of data for multi–modal retrieval has the biggest chances of success, particularly for clinical impact. Future fields of research are identified in the text, as there is a high research potential in the medical multimedia domain
THRESHOLDING-BASED SEGMENTATION AND APPLE GRADING BY MACHINE VISION
No full textIn this paper, a computer vision based system is introduced to automatically grade apple fruits. Segmentation of defected skin is done by three global thresholding techniques (Otsu, isodata and entropy). Stem-end/calyx regions falsely classified as defect are removed. Segmentations were visually best with isodata technique applied on 750nm filter image. Statistical features are extracted from the segmented areas and then fruit is graded by a supervised classifier. Linear discriminant, nearest neighbor, fuzzy nearest neighbor, adaboost and support vector machines classifiers are tested for fruit grading, where the latter outperformed others with 89 % recognition. 1
An Evaluation on the Robustness of Five Popular Keypoint Descriptors to Image Modifications Specific to Laser Scanning Microscopy
No full textLaser scanning microscopy (LSM) techniques are of paramount importance at this time for key domains such as biology, medicine, or materials science. Computer vision methods are instrumental for boosting the potential of LSM, providing reliable results for important tasks, such as image segmentation, registration, classification, or retrieval in a fraction of the time that a human expert would require (at similar or even higher accuracy levels). Image keypoint extraction and description represent essential building blocks of modern computer vision approaches, and the development of such techniques has gained massive interest over the past couple of decades. In this paper, we compare side-by-side five popular keypoint description techniques, scale invariant feature transform (SIFT), speeded-up robust features (SURF), binary robust invariant scalable keypoints (BRISK), fast retina keypoint (FREAK) and BLOCK, with respect to their capacity to represent in a reproducible manner image regions contained in LSM data sets acquired under different acquisition conditions. We evaluate this capacity in terms of descriptor matching performance, using data sets acquired in a principled manner and a thorough Precision-Recall analysis. We identify which of the five evaluated techniques is most robust to specific LSM image modifications associated to the laser beam power, photomultiplier gain, or pixel dwell, and show that certain pre-processing steps have the potential to enhance keypoint matching
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