Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices

Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on the scales of different ground objects. To guarantee image segmentation of ground objects with their own scale information, spectral indices that can be used to enhance some types of land cover, such as green cover and water bodies, were introduced into marker generation for the watershed transformation. First, a vector field model was used to determine the gradient of a multispectral remote-sensing image, and a marker was generated from the gradient. Second, appropriate spectral indices were selected, and the kernel density estimation was used to generate spectral-index marker images based on the analysis of spectral indices. Third, a series of mathematical morphology operations were used to obtain a combined marker image from the gradient and the spectral index markers. Finally, the watershed transformation was used for image segmentation. In a segmentation experiment, an optimal threshold for the spectral-index-marker generation method was identified. Additionally, the influence of the scale parameter was analyzed in a segmentation experiment based on a five-subset dataset. The comparative results for the proposed method, the commonly used watershed segmentation method, and the multiresolution segmentation method demonstrate that the proposed method yielded multispectral remote-sensing images with much better performance than the other methods.

Application of Biomimetic Nanomaterials in Biological Detection and the Intelligent Recognition Method of Nanoparticle Images

New nanomaterials (metal nanoclusters, graphene, etc.) are favored by researchers due to their unique properties and are widely used in biomedical detection. The excellent fluorescence characteristics of gold nanoclusters are utilized to develop a fast and highly sensitive bionic nanomaterial with non-label and dual functions, which can detect silver ions and mercury ions and study the particularity of TEM nanoparticle images. The particle segmentation of TEM nanoparticle images is studied to compare the traditional watershed algorithm and watershed transformation algorithm. The experiment results show that silver ions can enhance the fluorescence of gold nanoclusters to form gold-silver nanoclusters with strong yellow fluorescence, and mercury ions can quickly weaken the fluorescence of gold-silver nanoclusters. Based on the biomimetic nanomaterials, a dual-function fluorescent probe is designed to detect silver ions and mercury ions in lake with detection accuracy of 8 nM and 33 nM respectively; the sensing excitation of the fluorescent probe is further analyzed. Because the metal-enhanced fluorescence (MEF) effect enables the silver element and Au nanoparticles to form fluorescence-enhancing effect, the high metalphilic interaction between mercury ions and silver ions quenches the fluorescence effect of gold nanocluster; the rapid watershed transformation/region fusion method can achieve better particle image segmentation combined with the image segmentation algorithms of different TEM nanoparticles, which can be better applied to the characterization analysis of the preparation of gold nanomaterials.

Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization.

Utilization of Topographic Imagery using Watershed Transformation in Watersheds Prone to Natural Disasters

Indonesia is an archipelago that has abundant natural resources, however, problems arise in the process of utilizing natural resources, namely the emergence of natural disasters that have the potential to cause serious damage in several areas. The threat of floods and landslides in watersheds has become the main focus to be addressed as early as possible with the best solutions and planning. The use of topographic imaging in the field of remote sensing is one solution that is very useful in developing better natural disaster management systems. With the support of the use of the watershed transformation method, this study aims to obtain geographical situation data, both from the flow dimensions and slope conditions that affect the watershed discharge capacity. Thus, the risk of natural disasters can be minimized both from the level of material and non-material damage as early as possible.

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method.