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J. Imaging, Volume 5, Issue 9 (September 2019)

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Cover Story (view full-size image) Machine learning has been used to assist radiologists in making accurate decisions to diagnose and [...] Read more.
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Open AccessArticle
Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier
J. Imaging 2019, 5(9), 76; https://doi.org/10.3390/jimaging5090076 - 12 Sep 2019
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Abstract
This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and [...] Read more.
This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 . Full article
(This article belongs to the Special Issue Medical Image Understanding and Analysis 2018)
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Open AccessArticle
Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy
J. Imaging 2019, 5(9), 75; https://doi.org/10.3390/jimaging5090075 - 12 Sep 2019
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Abstract
This paper describes an unsupervised algorithm, which segments the nuclear envelope of HeLa cells imaged by Serial Block Face Scanning Electron Microscopy. The algorithm exploits the variations of pixel intensity in different cellular regions by calculating edges, which are then used to generate [...] Read more.
This paper describes an unsupervised algorithm, which segments the nuclear envelope of HeLa cells imaged by Serial Block Face Scanning Electron Microscopy. The algorithm exploits the variations of pixel intensity in different cellular regions by calculating edges, which are then used to generate superpixels. The superpixels are morphologically processed and those that correspond to the nuclear region are selected through the analysis of size, position, and correspondence with regions detected in neighbouring slices. The nuclear envelope is segmented from the nuclear region. The three-dimensional segmented nuclear envelope is then modelled against a spheroid to create a two-dimensional (2D) surface. The 2D surface summarises the complex 3D shape of the nuclear envelope and allows the extraction of metrics that may be relevant to characterise the nature of cells. The algorithm was developed and validated on a single cell and tested in six separate cells, each with 300 slices of 2000 × 2000 pixels. Ground truth was available for two of these cells, i.e., 600 hand-segmented slices. The accuracy of the algorithm was evaluated with two similarity metrics: Jaccard Similarity Index and Mean Hausdorff distance. Jaccard values of the first/second segmentation were 93%/90% for the whole cell, and 98%/94% between slices 75 and 225, as the central slices of the nucleus are more regular than those on the extremes. Mean Hausdorff distances were 9/17 pixels for the whole cells and 4/13 pixels for central slices. One slice was processed in approximately 8 s and a whole cell in 40 min. The algorithm outperformed active contours in both accuracy and time. Full article
(This article belongs to the Special Issue Medical Image Understanding and Analysis 2018)
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Open AccessArticle
Measurement of Vibrating Tympanic Membrane in an In Vivo Mouse Model Using Doppler Optical Coherence Tomography
J. Imaging 2019, 5(9), 74; https://doi.org/10.3390/jimaging5090074 - 04 Sep 2019
Viewed by 373
Abstract
Optical coherence tomography (OCT) has a micro-resolution with a penetration depth of about 2 mm and field of view of about 10 mm. This makes OCT well suited for analyzing the anatomical and internal structural assessment of the middle ear. To study the [...] Read more.
Optical coherence tomography (OCT) has a micro-resolution with a penetration depth of about 2 mm and field of view of about 10 mm. This makes OCT well suited for analyzing the anatomical and internal structural assessment of the middle ear. To study the vibratory motion of the tympanic membrane (TM) and its internal structure, we developed a phase-resolved Doppler OCT system using Kasai’s autocorrelation algorithm. Doppler optical coherence tomography is a powerful imaging tool which can offer the micro-vibratory measurement of the tympanic membrane and obtain the micrometer-resolved cross-sectional images of the sample in real-time. To observe the relative vibratory motion of individual sections (malleus, thick regions, and the thin regions of the tympanic membrane) of the tympanic membrane in respect to auditory signals, we designed an experimental study for measuring the difference in Doppler phase shift for frequencies varying from 1 to 8 kHz which were given as external stimuli to the middle ear of a small animal model. Malleus is the very first interconnecting region between the TM and cochlea. In our proposed study, we observed that the maximum change in Doppler phase shift was seen for the 4 kHz acoustic stimulus in the malleus, the thick regions, and in the thin regions of the tympanic membrane. In particular, the vibration signals were higher in the malleus in comparison to the tympanic membrane. Full article
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Open AccessArticle
Multi-Focus Image Fusion and Depth Map Estimation Based on Iterative Region Splitting Techniques
J. Imaging 2019, 5(9), 73; https://doi.org/10.3390/jimaging5090073 - 02 Sep 2019
Viewed by 276
Abstract
In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map. Compared with traditional methods (e.g., pixel- and block-based techniques), our focus-based measures are calculated [...] Read more.
In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map. Compared with traditional methods (e.g., pixel- and block-based techniques), our focus-based measures are calculated based on irregularly shaped regions that have been refined or split in an iterative manner, to adapt to different image contents. An initial all-focus image is first computed, which is then segmented to get a region map. Spatial-focal property for each region is then analyzed to determine whether a region should be iteratively split into sub-regions. After iterative splitting, the final region map is used to perform regionally best focusing, based on the Winner-take-all (WTA) strategy, i.e., choosing the best focused pixels from image stack. The depth image can be easily converted from the resulting label image, where the label for each pixel represents the image index from which the pixel with the best focus is chosen. Regions whose focus profiles are not confident in getting a winner of the best focus will resort to spatial propagation from neighboring confident regions. Our experiments show that the adaptive region-splitting algorithm outperforms other state-of-the-art methods or commercial software in synthesis quality (in terms of a well-known Q metric), depth maps (in terms of subjective quality), and processing speed (with a gain of 17.81~40.43%). Full article
(This article belongs to the Special Issue Modern Advances in Image Fusion)
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Open AccessArticle
Improvement of the Non-Destructive Testing of Heritage Mural Paintings Using Stimulated Infrared Thermography and Frequency Image Processing
J. Imaging 2019, 5(9), 72; https://doi.org/10.3390/jimaging5090072 - 29 Aug 2019
Viewed by 339
Abstract
Within the framework of conservation and assistance for the restoration of cultural property, a method of analysis assistance has been developed to help in the restoration of cultural heritage. Several collaborations have already demonstrated the possibility of defects detection (delamination, salts) in murals [...] Read more.
Within the framework of conservation and assistance for the restoration of cultural property, a method of analysis assistance has been developed to help in the restoration of cultural heritage. Several collaborations have already demonstrated the possibility of defects detection (delamination, salts) in murals paintings using stimulated infrared thermography. One of the difficulties encountered with infrared thermography applied to the analysis of works of art is the remanence of the pictorial layer. This difficulty can sometimes induce detection artifacts and false positives. A method of thermograms post-processing called PPT (pulse phase thermography) is described. The possibilities offered by the PPT in terms of reducing the optical effects associated with the pictorial layer are highlighted first with a simulation, and then through experiments. This approach can significantly improve the study of painted works of art such as wall paintings. Full article
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