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Journal of Imaging, Volume 5, Issue 9

September 2019 - 5 articles

Cover Story: Machine learning has been used to assist radiologists in making accurate decisions to diagnose and classify breast cancer at an early stage. We introduced classification models based on stack generalization that can help radiologists to predict whether microcalcification (MC) clusters are benign or malignant. Clinically grounded features of MC clusters were extracted from the segmented images. We provided an insight into how different meta-classifiers associated with stack-generalization models can improve the classification accuracy. The best classification accuracy was reported for the Optimam Mammography Image Database (OMI-DB) with a classification accuracy of around 95.00 ± 0.57 %. View this paper

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Article

15 Citations

7,777 Views

17 Pages

Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy

Cefa Karabağ,
Martin L. Jones,
Christopher J. Peddie,
Anne E. Weston,
Lucy M. Collinson and
Constantino Carlos Reyes-Aldasoro

J. Imaging2019, 5(9), 75;https://doi.org/10.3390/jimaging5090075

12 September 2019

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 calc...

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Article

15 Citations

9,509 Views

24 Pages

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

Nashid Alam,
Erika R. E. Denton and
Reyer Zwiggelaar

J. Imaging2019, 5(9), 76;https://doi.org/10.3390/jimaging5090076

12 September 2019

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 fro...

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Article

5 Citations

5,309 Views

7 Pages

Measurement of Vibrating Tympanic Membrane in an In Vivo Mouse Model Using Doppler Optical Coherence Tomography

Deokmin Jeon,
Joon Ki Kim,
Mansik Jeon and
Jeehyun Kim

J. Imaging2019, 5(9), 74;https://doi.org/10.3390/jimaging5090074

4 September 2019

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...

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Article

4 Citations

5,730 Views

16 Pages

Multi-Focus Image Fusion and Depth Map Estimation Based on Iterative Region Splitting Techniques

Wen-Nung Lie and
Chia-Che Ho

J. Imaging2019, 5(9), 73;https://doi.org/10.3390/jimaging5090073

2 September 2019

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-bas...

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Article

16 Citations

5,457 Views

13 Pages

Improvement of the Non-Destructive Testing of Heritage Mural Paintings Using Stimulated Infrared Thermography and Frequency Image Processing

Kamel Mouhoubi,
Vincent Detalle,
Jean-Marc Vallet and
Jean-Luc Bodnar

J. Imaging2019, 5(9), 72;https://doi.org/10.3390/jimaging5090072

29 August 2019

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 pos...

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