Along with the global climate change, there is an increasing interest for its effect on pheno- logical patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the web- cam images, regions of interest are manually defined on these images by an expert and sub- sequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particu- lar, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcamimages. A semi-supervised approach selects pixels based on the correlation of the pixels’ time series of percentage greenness with a few prototype pixels. An unsuper- vised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Addition- ally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 web- cams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun . Executable exam- ple code is provided as supplementary material.
Figure 7. Difference to the baseline for each of the color pre-processing methodologies (texture and hybrid hand-designed descriptors). Color shows details about descriptor, shape about data set. The data are filtered on f = 4. The zero line represents the condition where no color pre-processing was applied. The use of color pre-processing caused loss of accuracy in the majority of the cases: the median of 12 of the 17 methodologies was below the baseline, and the upper quartile of 11 of the 17 was close or below the baseline. It should be noted that for both Macenko and Reinhard, the best results were recorded when T1—i.e., the non-histological target—was used. Ruifrok and Johnston’s (decoRJ) was among the highest results, together with the relatively simpler chroma and gw.
Leukaemia stands for blood cancer that begins in the bone marrow and results in the generation of abnormal cells. Leukaemia is mainly classified as acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL) and chronic myeloid leukaemia (CML).This thesis makes an effort to devise a methodology for the detection and classification of Leukaemia. The images have been segmented using HSV colour based segmentation algorithm. The morphological components of normal and Leukemic lymphocytes differ significantly; hence various features are extracted from the segmented lymphocyte images, for detection purpose. The leukaemia is classified using SVM classifier.
Hyperspectral data was first manually inspected for data quality. Transects with corrupted or incomplete data (N = 43) were excluded. Transects deemed acceptable (N = 147) were rectified to represent the imaged scene as approximately square pixels. Rectification is necessary because the HyperDiver generates variable longitudinal and transverse resolution based on the imaging optics, swimming speed and frame acquisition rate . Furthermore, since it is an underwater system without georeferencing capabilities, image rectification could not be easily automated. The rectification was performed through cropping unwanted sections at the ends of the captured transect scene as well as stretching of the scan in the y direction to produce nearly square pixels and a visually coherent image of the scene. The spectrum in each pixel was linearly interpolated to 400 bands in the 400 nm to 800 nm wavelength range, and its intensity scaled down from 16-bit to 8-bit radiometric resolution. The supplementary sensor data from the HyperDiver scan were also included in the dataset, but the use of these data should be approached with care as they have not been evaluated beyond calibration. These include photosynthetically active radiation (PAR), altitude, pressure, and system pose data like pitch, roll, yaw and acceleration. An underwater video camera was also mounted onto the HyperDiver to capture high-definition videos of the scene during each scan. As part of HyperDiver data processing, these videos were color-corrected to ease taxonomic identification. Video frames were extracted at 3 s intervals and are included in this dataset.
Abstract: Automated Detection for Human Cancer Cell is one of the most effective applications of image processing and has obtained great attention in latest years, therefore. In this study, we propose an automated detection system for human cancer cells based on breast cancer cells. This study was conducted on a set of Fine Needle Aspiration (FNA) biopsy microscopic images that have been obtained from the ―Pathology Center - Faculty of Medicine - Mansoura University Hospital - Egypt‖ is made up of 72 microscope image samples of benign, 72 microscope image samples of malignant. The purpose of this study is to detect and classify the benign and malignant cells in the breast biopsy. The images are exposed to a series of pre- processing steps, which include resizing image such as 1024*1024, 512*512, enhance images by remove noise through (Median Filter) and contrast enhancement through (Unsharp Masking – Adjust Intensity). The system depends on breast cancer cells detection using clustering-based segmentation (K-means clustering, Fuzzy C-means clustering) and region-based segmentation (Watershed). Shape, Texture and Color features are extracted for Detection. The results show high Detection Rate for breast cancer cells images either (Benign or Malignant). Finally classification stage by using (Support Vector Machine, K-Nearest Neighbors and Back-Propagation Neural Networks). The final classification with the best accuracy in SVM is (97.22%), in K-NN and BPNNs is (98.61%).
Recently, deep learning has attracted much attention in many fields, such as image recognition and biomedical image analysis. Convolutional neural network (CNN) is one of the most popular algorithms for deep learning. CNN has been successfully applied to various fields and has achieved state-of-the-art performance in image recognition and classification [10, 11]. After its success, CNN is also exploited in medical fields, such as image processing and CAD [12-15]. Of these studies, some researches on pulmo- nary diseases have been conducted [16-19]. In these studies, interstitial lung disease pattern detection [16, 17], automatic detection of pulmonary nodules , and detection of ground glass opacity regions  using CNN are discussed. But there is still room for improvement on performance. We believe that en- hancing invariance of image features is a way to improve the performance.
Starting with 2015, we have the first major difference, the image crop size. All dataset im- ages are either 168 × 168 × 3 (inspired by best practices and appropriateness to the problem), and 224 × 224 × 3 (inspired by AlexNet). This resulted in two similar datasets, at two different crop sizes. The sets contain 5,250 unique negative images, randomly sampled from the proposed re- gions that do not contain elephants, mapped from IR using the manually adjusted homography method. The base of the positive images is from the 875 positive crops (visible, partially visible, and calf ) from the ground truth set. The RGB coordinates were created using the manually ad- justed homography method. These 875 positive images were augmented five times, resulting in 5,250 positive images (including the originals). The choice to only augment five times was based on memory usage and training time considerations. To augment the data we used a maximum translation range of 32px (20% of 168px) vertically and horizontally, a maximum rotation range of 360 degrees, a scaling range of − 20% to 20%, and a Bernoulli trial to horizontally or vertically flip the image. The augmentations were made before the images were cropped, with the inten- tion to introduce new information into the system through additional background data, and to ensure the two datasets were as similar as possible. Once the 5,250 positive and 5,250 negative images were assembled, we split them into training and validation sets. We allocated 80% of the images to the training set (4,200 positive, 4,200 negative), and 20% of the images to the validation set (1,050 positive, 1,050 negative). We ensured that an original positive image and all its aug- mentation were in the same set to reduce bias. All of the augmentation code was written from scratch and performed using standard image processing libraries in Python. Examples of the augmented positive data can be seen in Figure 4.1, with the original marked in blue followed by its augmentations.
Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cam- eras has received wide interest. With broad spatial coverage and high temporal resolution, networked cam- eras can provide information about snow cover and vegetation status, serve as ground truths to Earth ob- servations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cam- eras can also play an important role in supplementing laborious phenological field surveys and citizen sci- ence projects, which also suffer from observer-dependent observation bias. We established a network of dig- ital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.
More recent work, in 2007, proposed a system to distinguish vessels into arteries or veins by a prevailing vessel division model where a selected few were manually categorized initial divisions. Grisan and Ruggeri were one of the first ones to propose the methodology of automated AV classification. They suggested that the vasculature be divided using ‘vessel tracking and analysis’ where the vessel centerlines need to be identified. After outlining the ROI (a certain area of significance) around the optic disc and splitting this region into four sections, and eventually classifying into AV by an unsupervised clustering manner. This is done with the aid of features centered on color extracted from the vessel divisions. On 24 images, a total error rate of 12.4% was found using this procedure.
Digital Pathology has grown considerably in recent years encompassing computer- based activities that allow for improvements and innovations in the workflow of pathology . In this domain the automatedprocessing of tissue samples has received increasing attention due to the potential applications in diagnosis , grading , identification of tissue substructures , prognostication and mu- tation prediction . A number of problems, however, still limit the adoption of digital pathology on a large scale: the relatively scarce availability of large la- belled datasets of histological images, the differences in the acquisition systems and/or protocol used as well as the variability in tissue preparation and/or stain reactivity . The latter, in particular, can generate colour variations and arti- facts that can reduce significantly the accuracy of computer-based methods. This
imaging refers to technologies used to view the human body for diagnosis or treatments or monitoring. Information from the area of the body being analyzed can be related to possible diseases for helping treatments of the same. The field of medicinal imaging has gained importance due to mechanizations, non-invasiveness and speed. These technologies have been helpful in advancing image processing, analyses and prediction of diseases. Imaging modalities can generate pictures from different planes, thus allowing specialists to have a close introspect on suspected areas during analysis. It has turned into a generally utilized system for great therapeutic imaging, particularly in mind imaging where delicate tissue contrast and non-obtrusiveness are clear points of interest. X-ray is analyzed by radiologists focused around visual translation of the movies to recognize the vicinity of unusual tissue. Mind pictures have been chosen for the picture reference for this examination on the grounds that the wounds to the cerebrum have a tendency to influence substantial ranges of the human brain part. The classification of information is vital to prune ordinary patients and analyze likelihood of anomalies or tumors in other patients. Thus it is becomes to process and extract information from medical images for identifying tumors in the brain and this paper proposes a novel way of processingimages for tumor analysis.
Artificial Intelligence (AI) and related computational tools are making their presence felt in various walks of life. However, AI in healthcare is grabbing more attention in latest trends of research with improved accuracy of I based techniques for different health related problems. This paper presents a technique for brain tumor classification using an amalgamation of image processing techniques and artificial intelligence. Brain tumors are often very difficult to classify into malignant and benign categories owing to the high level of similarity among the two categories of images. The proposed technique uses the discrete wavelet transform along with threshold based segmentation for separation and de- noising of brain tumor images. Further, feature extraction is performed followed by training a probabilistic neural network with the computed feature values. Principal component analysis is used for reduction of the dimensionality of the training data. It has been found that the proposed technique achieves a classification accuracy of 98% for the used data set. It is expected that the proposed approach can be useful for effective automatedclassification of brain tumor images.
bioluminescence imaging, and fluorescence imaging offer some morphological as well as functional information, they lack the ability to assess and track in vivo biological phenomenon, a pivotal link for greater mechanistic understanding following cell-based intervention. This approach discusses currently available in vivo imaging modalities and image processing techniques, which may support this field of research. Images are often corrupted during acquisition and transmission, as a consequence of the imaging modality and the communication media[119,120]. Pre-processing tries to correct for problems such as these[119,120,121,122] and prepares of the image for the main Image Processing tasks such as feature extraction. Typical pre-processing tasks include noise removal, intensity adjustment, contrast enhancement, interference removal as shown in Figure 2.4, it is an example, due to uneven illumination over the field of view, there is a large variation in image intensity that must be removed using contrast enhancement methods. Figure 2.4(D) shows the application of image equalization for contrast enhancement using Image Histogram and Intensity Adjustment Demo in MATLAB.
The database of OCT was obtained from Al-hayaa advanced radiology centres .The entire method presented in this paper was implemented using MATLAB 7.0, and makes extensive use of the Image Processing Toolbox. We have main steps in this current study. The algorithm of this study is shown in figure (1) below. The image is obtained from SD-OCT, then it will be taken to filter out the speckle noise which we are concerned in preprocessing step. Diffusion filter is used to clear the noise. The filtered image will be taken to the important three steps which are segmentation, feature extraction and classification.
An Artificial Neural Network (ANN) is a computational model based on the structure and functions of biological neural networks. Here, two layer feed forward network is used. A feed forward neural network  is an artificial neural network wherein connections between the units do not form a cycle. It consists of a large number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with all the units in the previous layer. The weights on these connections encode the knowledge of a network. Often the units in a neural network are also called nodes. Data enters at the inputs and passes through the network, layer by layer, until it arrives at the outputs. During normal operation, that is when it acts as a classifier, there is no feedback between layers. SIFT based feature extraction method produces recognition efficiency of 96% efficiency with ANN.
CCD camera was used acquired the images for our image processing software. It was found that any variation in luminance and any vibration of stage adversely affect the system performance. We took around 2ml of blood on to an EDTA tube which prevents the blood cell from rouleaux formation and platelet activation since we needed the isolated healthy cells in theimage.We took 10µl of blood and mix it with 200µl saline hence 20 times dilution was achieved. This was done in order to separate cells from each other. 20 µl of thediluted blood sample is poured on to simple hemocytometer. It was carefully placed under a microscope which has an inbuilt camera so the image can be stored onto a system. Here we use SISMAX 800i to count cells for verification. We conducted above-mentioned study 30 times with 20 different blood samples and results are given in the table below:
M muthu rama krishnan et al.  proposed a new automated glaucoma diagnosis system using a combination of HOS, TT, and DWT features extracted from digital fundus images. The system, uses an SVM classifier (with polynomial kernel order 2), was able to detect glaucoma and normal classes with an accuracy of 91.67%, sensitivity of 90%, and specify of 93.33%. This classification efficiency may even be further improved using images with a broader range of disease progression, better features, and robust data mining algorithms. In addition, we propose an integrated index, which is composed of HOS, TT, and DWT features. The GRI is a single feature which distinguishes normal and glaucoma fundus images, Hence; it is a highly effective diagnostic tool which may help clinicians to make faster decisions during mass screening of retinal images. The proposed system is cost effective, because it integrates seamlessly with digital medical and administrative processes and incorporates inexpensive general processing components. Therefore, the glaucoma detection system can be used in mass screening where even a modest cost reduction, in the individual diagnosis, amounts to considerable cost savings. Such cost savings may help to eliminate suffering, because the money can be used to increase the pervasiveness of glaucoma screening or it can be used anywhere else in the health service, where it is even more effective.
Fig. 5 each crater is shown twice. The larger panel displays topography as rendered from the DEM with spatial resolution of 500 m=pixel. The smaller panel shows a portion of the same crater as it appears on an image with resolution of 100 m=pixel. The scale and the orientation of shaded relief and images are not the same. The higher resolution of images helps to conﬁrm that wall breaches in these craters are due to the ﬂuvial disruption. The origin of the breach located at the 8 o’clock direction in crater D is difﬁcult to determine from the shaded relief alone, but an image clearly shows that it is due to a channel that merges with the crater. The topography of the terrain indicates that the breach is an inlet. In the Noachian epoch, water ﬂowed through that inlet into the crater D to create a lake. The location of the outlet was not identiﬁed, suggesting that the crater served as the terminal basin collecting water from the upstream terrain. A shaded relief of crater E clearly shows a channel merging with the crater at the 1 o’clock direction. The topography indicates this breach is an outlet. The image conﬁrms this ﬁnding and reveals the existence of an inlet located at the 4 o’clock direction. In the Noachian epoch this crater was a lake that constituted part of a larger drainage system.
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Following the protocol described, images will be uniform in intensity, shape, and size without extra-meningeal tissue. Notable milestones in the protocol are the “N3 Correction” and “Modal Scale” step, in which the histogram of the images will be altered to a standard for uniformity, necessary for alignments. Checking these key steps helps determine if the preprocessing is on track or not. Similarly, checking the mask helps to ensure that the skull stripping program has produced expected results. The skull-stripping step should create a series of white, binary masks which should fit over their corresponding whole-head images to create an image with the extra-meningeal tissue removed (Figure 11b). One common mistake here is when the output of the Skull Strip program is a different image type than the whole head image receiving the mask. If fuzzy borders occur, check the image type in FIJI and make sure both mask and whole head image are the same type. It is anticipated that the histogram after modal scaling will reveal co-incident peaks of the template and scaled images (Figure 8). Creation of the MDA is a notable milestone. As an average of all pre- injection images, it should have more fine detail that any individual pre-injection image (Figure 13). Check the MDA in the 3D image viewer and confirm that it is sharper, and not blurrier, than the input images. Our automated program is more consistent than manual preprocessing, and thus produces a sharper averaged image.