Automated Image Detection of Retinal Pathology

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The rest consists of two mains steps. The first step is the continuation of preprocessing which is done by applying histogram expansion and median filter. Then difference between the filtered image and his inverse yielded to the enhanced image. The second step begins by morphological closing operation which is applied on the enhanced image to suppress blood vessels. This is follow by the segmentation of exudates and optic disk by entropy maximization. Finally, base on size criteria, optic disk is eliminated and it remains the regions of interest which are exudates.

Details on the different steps are given below. The retinal images appear with low light, very noisy and carry a lot of information. Preprocessing images commonly involves removing low frequency background noise, normalizing the intensity of the individual particles images, removing reflections, and masking portions of images. Image preprocessing is the technique of enhancing data images prior to computational processing. Resizing is used to alter the size of an image.

Most images are in truecolor.

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To separate the color components with intensity and brightness, the component retinal input images in the RGB color space are converted to grayscale image by normalizing the range of the R, G, and B components to the interval from 0 to 1. We extract the green channel image, because green channel shows high intensity between exudates and background as compared to red and blue [13].

Filtering methods are used to separate the noise pixels from the regions of interest. In our project we use histogram expansion and median filter. Also called dynamic expansion, its purpose is simply to give a low-contrast image the full dynamic range that is allowed by a linear expansion of the gray scale used by the image. Usually a better contrast is obtained.

This method consists of making best use of the gray scale available on the image acquisition system. This comes down to extending the histogram so that the lowest value is at zero and the highest is at the maximum value. The linear transformation is described as follow. Let be the range of intensities of pixels present in the starting image and the available interval In general and. The modified pixel obtain after the dynamic expansion of pixel in the starting image is given by the relation:. This operation also leads to the reduction of the number of false negatives FN.

The Modified image still contains much noise reason why median filter is used in the next step. The median filter is a digital filter, often used for noise reduction. Noise reduction is a conventional preprocessing step to improve the results of future treatments edge detection, for example. The median filter technique is widely used in digital image processing because it reduces noise while maintaining the contours of the image. The main idea of the median filter is to replace each entry with the median value of its neighborhood. The median filter enables to eliminate the aberrant values without being limited to averaging them, which would tend to contaminate the neighboring values with this aberrance and make the image become blurred.

The median filter respects the contrast of the image if we multiply all the values by a positive constant, the ordering of the values is unchanged and the brightness of the image adding a constant does not modify the scheduling either. In areas where the intensity is monotonous only increasing or only decreasing the filter leaves the image unchanged. It respects the contours, and eliminates extreme values. The two operations in subsequent paragraphs are very important in the automatic detection of retinal exudates.

The preprocessing step ends with a simple difference between the filtered image and its inverse. An example can be seen on the retinal fundus image in Figure 3. On the original image Figure 3 a , the.

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Figure 3. Enhancement of an image by histogram expansion: a - c are respectively the starting image from DIARETDB1 dataset , the green channel image with poor contrast and the correspondent histogram; d and e are respectively the Modified image after dynamic expansion with better contrast and the correspondent histogram; f is the enhanced image obtain by applying median filter and subtraction operation to image d. Segmentation is the part of partitioning a digital image into multiple segments.

Image segmentation is the front stage processing, three advantages in image segmentation that are speed, good shape and reliable. In our project image segmentation consist into two steps: Optic disc detection, Blood vessels extraction. Blood vessels appeared as networks of either deep red or orange-red filaments which take origin within the optic disc and were of progressively diminishing width [14].

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The presence of this element in retinal image after preprocessing leaves the OD subdivided into several non-zero gray level regions. To give the OD a uniform distribution of gray level forming a single region, we proceed to the elimination blood vessels using a morphological closure that derives from the basic operators which are erosion and dilation. After removing the blood vessels, we obtain an image with the OD defining a single region and having a uniform distribution of gray levels. In this section, we will select and binarized the image candidate knowing that the exudates are homogeneous in them color property and are characterized by a strong and brightness contrast comparable to other image.

In the localization of exudates regions in digital retinal fundus images, it is essential that the expertise and experiences of the ophthalmologists be incorporated into computer visualisation processes for improvements in segmentation accuracy.

Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy

The problem we want to solve is to detect lesions in Retinal fundus images which appear as bright, spot-like protrusions in the breast tissue. These lesions represent small areas in the image. A method suitable for such situations is thresholding by entropy maximization. Therefore the entropy maximization will be applied to eliminate the background of the image.

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Entropy maximization algorithm is a popular technique of global thresholding derived from Schannon information theory [15]. In this technique, two classes problems are to be processed: one have typically a class with many candidates background pixels in our case and a class with a few candidates exudates and optic disk pixels.

Let and be classes of grey level in the original mammogram. Let T be the threshold that leads to the partition of the image into the two classes C 1 and C 2. Then the entropy of the image is evaluated by the following equation:. For various values of T which all lead to the partition of the image in two classes , we have different values of the sum.

The algorithm will finally retain the threshold T for which, the entropy reaches its maximum value. As can be seen, E is actually a function of T.

The value of T, which gives the maximal entropy value , is defined as threshold. The logarithms function in Equation 6 and Equation 7 improve classes for which frequency of apparition are low. Step 2: We group the grey level of the image into 2 classes with a threshold T chosen arbitrarily among pixels of our histogram. Step 3: We compute the entropy of the image for T varying from 0 to N. The OD is the point of the eye that does not see.

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  • It is a bright yellowish disk and elliptical contour in human retina from where the blood vessels and optic nerves emerge [16 , 17]. The optical disk has a larger size than the retinal exudates.


    To eliminate this element, we use a size-based criterion to remove in the binary image any region that has the maximum area. From enhanced image obtain during preprocessing step, we can see in the Figure 4 below exudates obtain after segmentation. This section presents the results of exudates segmentation from the proposed method.

    An example of a retinal fundus image and its detection result is shown in Figure 5. The following figure Figure 6 shows some images of the DIARETDB1 database with their manual segmentations made by the ophthalmologists and confirmed by biopsy and the results of the segmented exudates as well as the image with the segmented exudates reconstructed and superimposed on the original image. We carried out evaluation of the system we built in term of its capacity to detect all exudates. Then, the performance of our approach is estimated by comparing our results with the performance of ophthalmologists.

    Following the practices in the medical research, the fundus images related to the diabetic retinopathy.

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    Figure 4. Result of segmentation procedure. Figure 5. Result of segmentation procedure: a The starting image from DIARETDB1 dataset which includes exudates; b The green channel image with poor contrast; c Is the Modified image after dynamic expansion with better contrast; d Is the enhanced image obtain by applying median filter and subtraction operation to image c ; e f Result of ROIs segmented with EMT algorithm; f Is detected exudates; g Segmented exudates shown in white reconstructed on the original image. The tissue contains a breast cancer metastasis as well as macrophages, which look similar to a tumor but are benign normal tissue.

    Our algorithm successfully identifies the tumor region bright green and is not confused by the macrophages. Our algorithm was designed to be highly sensitive to make it easier for pathologists to find even small instances of breast cancer metastasis in lymph node biopsies. You can learn more about our findings in this paper and blog post. You can read more about our research collaborations in this blog post. Our efforts fall into three broad areas: 1 extending TensorFlow to better support genomics data; 2 developing deep learning models for genomics problems; and 3 releasing new tools and capabilities as open source software.

    Our first major project is DeepVariant , a universal SNP and small indel variant caller created using deep neural networks. DeepVariant is a collaboration between Google and Verily Life Sciences , and is available as open source. Background: The growing adoption of digital pathology enables remote image access or consults and the use of powerful image analysis algorithms.

    Like specimen processing, the slide digitization process can result in additional artifacts, including out-of-focus OOF regions of varying severity and size. OOF regions are often only detected upon attempted high power review of affected regions, potentially triggering rescanning and causing workflow delays.

    Although scan-time operator screening for whole-slide OOF is feasible, manual screening for OOF areas affecting only parts of the slide is intractable. Methods: In this study, we developed an Mermel , Jason Hipp , Martin Stumpe. Purpose Use adjudication to quantify errors in diabetic retinopathy DR grading based on individual graders and majority decision, and to train an improved automated algorithm for DR grading.

    Design Retrospective analysis. Participants Retinal fundus images from DR screening programs. Methods Images were each graded by the algorithm, U. The adjudicated consensus of the retinal specialists served as the reference standard. Main Outcome Measures For agreement between different graders as well as between the graders and the algorithm, we measured the quadratic-weight Ophthalmology Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito popula- tions.

    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology
    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology
    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology
    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology
    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology
    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology
    Automated Image Detection of Retinal Pathology Automated Image Detection of Retinal Pathology

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