Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

Iodine doping of a photocuring, one-component embedding medium for µCT and histology

Objective: Evaluation of μCT scans of bone implant complexes often shows a specific problem: if an implant material has a very similar radiopacity as the embedding medium (e.g. methacrylate resin), the implant is not visible in the μCT image. Segmentation is not possible, and especially osseointegration as one of the most important parameter for biocompatibility is not evaluable. Methods: To ensure μCT visualisation and contrast enhancement of the evaluated materials, the embedding medium Technovit® VLC7200 was doped with an iodine monomer for higher radiopacity in different concentrations and tested regarding to handling, polymerisation, and histological preparation, and visualisation in µCT. Six different µCT devices were used and compared with regard to scan conditions, contrast, artefacts, image noise, and spatial resolution for the evaluation of the bone-implant blocks. Results: Visualisation and evaluation of all target structures showed very good results in all μCT scans as well as in histology and histological staining, without negative effects caused by iodine doping. Subsequent evaluation of explants of in vivo experiments without losing important information was possible with iodine doped embedding medium. Conclusion: Visualisation of implants with a similar radiopacity as the embedding medium could be considerably improved. µCT scan settings should be selected with the highest possible resolution, and different implant materials should be scanned individually for optimal segmentation. µCT devices with higher resolutions should be preferred. Advances in knowledge: Iodine doped embedding medium is a useful option to increase radiopacity for better visualisation and evaluation of special target structures in µCT.

Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor

ObjectivesTo automate image delineation of tissues and organs in oncological radiotherapy by combining the deep learning methods of fully convolutional network (FCN) and atrous convolution (AC).MethodsA total of 120 sets of chest CT images of patients were selected, on which radiologists had outlined the structures of normal organs. Of these 120 sets of images, 70 sets (8,512 axial slice images) were used as the training set, 30 sets (5,525 axial slice images) as the validation set, and 20 sets (3,602 axial slice images) as the test set. We selected 5 published FCN models and 1 published Unet model, and then combined FCN with AC algorithms to generate 3 improved deep convolutional networks, namely, dilation fully convolutional networks (D-FCN). The images in the training set were used to fine-tune and train the above 8 networks, respectively. The images in the validation set were used to validate the 8 networks in terms of the automated identification and delineation of organs, in order to obtain the optimal segmentation model of each network. Finally, the images of the test set were used to test the optimal segmentation models, and thus we evaluated the capability of each model of image segmentation by comparing their Dice coefficients between automated and physician delineation.ResultsAfter being fully tuned and trained with the images in the training set, all the networks in this study performed well in automated image segmentation. Among them, the improved D-FCN 4s network model yielded the best performance in automated segmentation in the testing experiment, with an global Dice of 87.11%, and a Dice of 87.11%, 97.22%, 97.16%, 89.92%, and 70.51% for left lung, right lung, pericardium, trachea, and esophagus, respectively.ConclusionWe proposed an improved D-FCN. Our results showed that this network model might effectively improve the accuracy of automated segmentation of the images in thoracic radiotherapy, and simultaneously perform automated segmentation of multiple targets.

Optimizing the Segmentation of a High-Resolution Image by Using a Local Scale Parameter

Image segmentation is a critical procedure in object-based identification and classification of remote sensing data. However, optimal scale-parameter selection presents a challenge, given the presence of complex landscapes and uncertain feature changes. This study proposes a local optimal segmentation approach that considers both intersegment heterogeneity and intrasegment homogeneity, uses the standard deviation and local Moran's index to explore each optimal segment across different scale parameters, and combines the optimal segments into a single layer. The optimal segment is measured by using high-spatial-resolution images. Results show that our approach out-performs and generates less error than the global optimal segmentation approach. The variety of land cover types or intrasegment homogeneity leads to segment matching with the geo-objects on different scales. Local optimal segmentation demonstrates sensitivity to land cover discrepancy and provides good performance on cross-scale segmentation.

Enhancing Offline Tamil Handwritten Character Recognition Using Optimal Newton Algorithm Based Deep Convolution Extreme Learning Model

At present, recognizing Tamil characters is considered as one of the most provoking and challenging taskssince there exist discontinuities, slanting, huge differences as well as free-style property characters. In such cases, the error value is enhanced and most of the error arises due to the chaos between the characters having analogous shapes. In addition to this, the time required for processing is also increased. To overcome such shortcomings, recognition of Tamil characters is proposed comprising of four principal stages namely Pre-processing, Segmentation, Feature extraction and classification phase. In the initial data pre-processing phase, the input images are pre-processed by employing thresholding binarization, adaptive filter for noise elimination as well as cropping. Secondly, segmentation is employed typically for verifying an object as well as various boundaries like lines, curves, bends, etc. For optimal segmentation, this paper utilizes Tsallis entropy-based atom search (TEAS) optimization algorithm. Then the segmented features are fed to extract the features and finally in the classification phase, the Tamil characters are recognized effectively. Here, this paper utilizes deep convolution extreme learning-based Newton Metaheuristic (DCELM-NM) approach for both feature extraction and classification. The performances of the proposed approach are evaluated using various simulation measures to visualize the effectiveness. In addition to this, the comparative analyses are carried out and the results reveal that the proposed approach provides superior performance when compared with existing approaches.