Medical Imaging
The dataset includes annotated Computed Tomography (CT) scanned images. The labels consist of three types:
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184 Views
This dataset is a private foot pressure image dataset containing 317 images of high arches (H), 217 images of flat feet (L) and 362 images of normal feet (N).
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167 Views
Details as in the Dataset Image.
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76 Views
Relaxation mechanism of magnetic particles is crucial in differentiating particles and estimating temperature and viscosity for diagnosis and treatment. The magnetization recovery process in field flat phase of pulsed excitation generates decay signals that can be fitted by a bi-exponential model. The relaxation time spectrum can be generated by using inverse Laplace transform.
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113 Views
Hydrogel scaffolds have attracted attention to develop cellular therapy and tissue engineering platforms for regenerative medicine applications. Among factors, local mechanical properties of scaffolds drive the functionalities of cell niche. Dynamic mechanical analysis (DMA), the standard method to characterize mechanical properties of hydrogels, restricts development in tissue engineering because the measurement provides a single elasticity value for the sample, requires direct contact, and represents a destructive evaluation preventing longitudinal studies on the same sample.
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98 Views
This data contains 80 blood cell images with a resolution of 5472×3648. It is mainly a segmentation dataset of five categories of white blood cells, including lymphocytes, basophils, neutrophils, eosinophils and monocytes.
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28 Views
The data is divided into a training set of 999 images and a test set of 335 images. The size of each 2D ultrasound image is 800 by 540 pixels with a pixel size ranging from 0.052 to 0.326 mm. The pixel size for each image can be found in the csv files: ‘training_set_pixel_size_and_HC.csv’ and ‘test_set_pixel_size.csv’. The training set also includes an image with the manual annotation of the head circumference for each HC, which was made by a trained sonographer.
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977 Views
This dataset includes four sub-datasets: Drishti-GS, RIM-ONE-r3, ORIGA and REFUGE. Each image is cropped around the optic disc area for joint optic disc and cup segmentation. The size of all images is 512×512. The manual pixel-wise annotation is stored as a PNG image with the same size as the corresponding fundus image with the following labels:
128: Optic Disc (Grey color)
0: Optic Cup (Black color)
255: Background (White color)
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968 Views
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. One of the major stumbling blocks for manual retinal examination is the lack of a sufficient number of qualified medical personnel per capita to diagnose diseases.
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5360 Views
Abstract
This synthetic dataset or phantom consists of 6 raw format databases, in the three–dimensional (3-D) domain, which are identified as follows:
DB1: Ground Truth
DB2: Poisson noise
DB3: Stair-step artifact
DB4: Streak artifact
DB5: Both artifacts
DB6: Hybrid.
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250 Views