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Mathematical morphology offers a rich framework for segmenting images and video sequences. We propose a unified presentation of these tools, expressed as a small number of distance transforms on graphs. The classical approach based on watershed and markers is reinterpreted in a multiscale framework and new results are presented, considerably enlarging the palette of available tools.

A robust image segmentation method that combines the watershed segmentation and penalized fuzzy Hopfield neural network (PFHNN) algorithms to minimize undesirable over-segmentation is described in this paper. This method incorporates spatial graph representation derived from the watershed segmented regions and cluster analysis information obtained from the PFHNN algorithm to achieve efficient image segmentation. The proposed scheme employs the Markov random field (MRF) model on the region adjacency graph (RAG) to improve the quality of watershed segmentation. Here, the fusion criterion is according to the correlation coefficient, which uses inter-region similarities to determine the merging of regions. Analysis of the performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images, and significant and promising segmentation results are presented using brain phantom simulated data.

Watershed transformation has proven to be an important tool in image analysis. However, the resulting image of watershed transformation is inevitably over-segmented due to the presence of noise or local irregularities in the input image. In this paper, the use of contour altitude at the immersion stage is proposed. Block gradient information computed from the input gradient image is defined and used to obtain a critical altitude of watershed flooding. This altitude is then refined based on entropy estimated from the intermediate segmentation result. Thereafter, an optimal altitude and its corresponding segmentation result can be obtained. Although this process can favorably reduce the number of regions, the quality of segmentation still requires further improvement. Hence, a Markov Random Field (MRF) model defined on a region adjacency graph (RAG) is adopted. Because the MRF model can merge neighboring regions that are similar in local statistic properties, it thus alleviates the over-segmentation problem and improves the quality of image segmentation. In the experimental studies, the proposed method has been tested using several benchmark images. It achieves improved appearance and energy indices in comparison with the results obtained by conventional methods.

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Measurement of fetal femur length is crucial for the estimation of fetal age and growth pattern. Due to the noisy nature of ultrasound images and the variation in image acquisition and measurement techniques, manual measurements are subject to interobserver and intraobserver variability. The motivation of this paper is to study the efficiency of automated methods used in the femur endpoint detection module of our proposed computer-assisted processing and analysis system, which is employed to determine the gestational age for promoting the standardization of fetal femur measurements, thus resulting in more reliable age estimates. The proposed methods are based on the morphological operators methods⁵ and the morphological watershed segmentation method. We applied these methods on a set of 100 digitized fetal femur images of size 287 × 224 pixels with 256 gray-level images and a resolution of 100 pixels per inch (ppi) horizontally and vertically. The results showed that the watershed method was the more efficient one and gave reliable results for every femur image tested.

Automated analysis and interpretation of retinal images has become an incontournable diagnostic step in ophthalmology. Retinal blood vessels morphology can be an important indicator for diseases such as diabetic retinopathy; and their detection also serves for image registration. This paper presents a method based on mathematical morphology for extraction of vascular tree in color retinal image with low contrast. It consists in contrast enhancement and application of watershed transformation in order to segment blood vessels in digital fundus images.

Strategic Environmental Assessment (SEA) and Cumulative Effects Assessment (CEA) are the focus of this research that investigated the evolving response in plans to the biologically undesirable accumulation of copper and zinc in a New Zealand estuary. Sources of metals are influenced by policies and plan provisions for land use, transport, stormwater management and boat moorings. During four decades of local government reform, plans responded to the scientific evidence of accumulation with increasingly sophisticated stormwater management. Despite the less explicit inclusion of SEA in New Zealand legislation, this research demonstrates for international audiences the concurrence of jurisdictional amalgamation; growing awareness and knowledge sharing across scientific, engineering and planning practitioners; and a steady improvement in plans to enable a marked slowing in cumulative effects (CEs) of urbanisation. A proposed Auckland Unitary Plan (PAUP) attempts to address some cumulative effects within watersheds now no longer divided by jurisdictional boundaries.

This study was carried out to design an incentive payment for an ecosystem services (IPES) scheme in the Baitadi Town Water Supply and Sanitation Project of Nepal. The main intention behind the designing of the scheme was to develop strategy for equitable use of water resources and involve communities, watershed and water user, in the sustainable management of water resources. We administered household survey in both the watershed community and water users to elicit their preferences regarding water source management and drinking water supply. A discrete choice experiment was employed in the case of water users which showed that, for them, water quality and quantity are the most important attributes. The estimated annual willingness-to-pay of water users for doubling water availability is NPR 482,076 (USD 4,505) and for doubling the water quantity and the supply of clean water that can be drunk directly from the tap is NPR 1.18 million (USD 10,988). The results of consultations with stakeholders indicate that the construction of public toilets, the regularization of grazing, off-season vegetable farming and drinking water distribution in the upstream area may contribute to maintaining the quality of water while keeping the watershed community satisfied with regard to water-sharing. These activities require NPR 1.17 million (USD 10,987) in the first year and NPR 425,640 (USD 3,978) annually from the second year on. The estimated willingness-to-pay and cost of the watershed activities indicate that implementing IPES in the Baitadi Town Water Supply Project is financially feasible and socially acceptable. Our study recommends the integration of the IPES design into the project design phase in future drinking water scheme, the best option being its integration into the initial environmental examination at the time of project design.

The COVID-19 pandemic originated in Wuhan, China in December 2019 and has since affected over 200 countries worldwide. The highly contagious Coronavirus primarily affects the respiratory system, causing pulmonary inflammation that can be visualized through medical imaging such as CT and X-rays. Conventional testing methods include PCR and antibody tests. Shortage of test kits in hospitals as well as time taken for results to be received can be compensated through medical imaging. Therefore, there is a need for an automated system, which is accurate and robust in detection of Covid-19 from medical radiographs for clinical practice. The objectives of our study are as follows: (i) To segment the lung CT images using a hybrid watershed and fuzzy c-means algorithm. (2) To extract various textural features using the GLCM algorithm. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. Our dataset consisting of 60 chest CT images of COVID-19 and non-COVID-19 patients was pre-processed and segmented using a hybrid watershed and fuzzy c-means algorithm. Then, textural features were extracted from the segmented ROI using the GLCM algorithm. Finally, the images were classified into COVID and non-COVID classes using three machine learning classifiers namely Naïve Bayes, SVM and K-star. Naïve Bayes classifier achieved the highest accuracy of 95%, while SVM achieved 93% accuracy. The ROC curves were also obtained, with AUC of 0.98. Thus, our proposed system has shown promising results in the classification of lung CT images into the two classes namely COVID and non-COVID.

When the Salmon Spoke is part of an ongoing initiative by its creators to cultivate tribal and First Nations relationships across imposed political borders and amplify Indigenous voices fighting for wild salmon and sustainable mining practices in the “transboundary” region of Southeast Alaska and Northern British Columbia. When the Salmon Spoke features first-person stories from communities of the Stikine River watershed, including coastal Tlingit and inland Tahltan. Their stories are illustrated and supported by cinematic place-based imagery, Indigenous music and visual art, and additional artistic work contributed by non-Indigenous allies. When the Salmon Spoke utilizes the power of storytelling with the understanding that it is a foundation to learning and a practice that sustains community while validating experiences and Indigenous epistemologies. We argue that this Indigenous storytelling methodology is integral because there is a long-standing tradition in Western society that imposes Western stories as universal truths, tells the stories of others and misrepresents non-Western narratives. This community-engaged approach, co-led by Indigenous culture bearers and their allies, explores the ways in which transboundary peoples experience and respond to the ongoing looming mineral claims. When the Salmon Spoke demonstrates the ways in which communities can reclaim their relations to one another, land, and culture. This dual form of storytelling — both performative and archival — creates a cultural moment for change, awareness and transboundary solidarity to protect critical and threatened salmon watersheds in the face of aggressive industrial-scale mining in BC. This storytelling project encourages the protection of vital salmon-bearing watersheds while reinforcing the inextricable ties to a sense of place and oral history amongst trans-boundary nations and communities.

Numerous defects can occur in the process of manufacturing display panels. In particular, a defect called mura is one of the most difficult defects to detect using conventional image processing algorithms. It usually appears as relatively dark or relatively bright regions with very low contrast against the background when the panel is viewed by an imaging device. We have developed a fast mura defect detection method based on the conventional watershed algorithm. The flooding step of the watershed algorithm is carefully re-designed to detect mura defects that exist both inside and at the boundary of an image. The just noticeable difference (JND) technique is used to quantify the level of the mura defects. Experiments show that the proposed algorithm is efficient and provides rapid detection of low-contrast mura defects.