Level Set Method Based on Improved Mumford-Shah Model Applied in Wood Cell Image Segmentation

Guan, Xuemei; Sun, Lin; Cao, Jun

doi:10.1109/ijcnn.2006.247031

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Since approaches based on this method tend to over-segment vessels, they require pre-processing (e.g., filtering operations [28,29]) and post-processing operations (e.g., merging regions [30]). Other works use more sophisticated methods for segmenting wood vessels, such as level sets based on Mumford-Shah model [31], gradient vector flow snake [32]. In general, these methods tend to have problems with low contrast areas, as well as being very sensitive to the initial position of the level set function that makes them prone to converge to false edges.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images

García‐Pedrero

García‐Cervigón

Olano

et al. 2019

Neural Comput & Applic

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Xylem is a vascular tissue that conducts sap (water and dissolved minerals) from the roots to the rest of the plant while providing physical support and resources. Sap is conducted within dead hollow cells (called vessels in flowering plants) arranged to form long pipes. Once formed, vessels do not change their structure and last from years to millennia. Vessels' configuration (size, abundance, and spatial pattern) constitutes a record of the plant-environment relationship, and therefore, a tool for monitoring responses at the plant and ecosystem level. This information can be extracted through quantitative anatomy; however, the effort to identify and measure hundreds of thousands of conductive cells is an inconvenience to the progress needed to have solid assessments of the anatomical-environment relationship. In this paper, we propose an automatic methodology based on convolutional neural networks to segment xylem vessels. It includes a post-processing stage based on the use of redundant information to improve the performance of the outcome and make it useful in different sample configurations. Three different neural networks were tested obtaining similar results (pixel accuracy about 90%), which indicates that the methodology can be effectively used for segmentation of xylem vessels into images with non-homogeneous variations of illumination. The development of accurate automatic tools using CNNs would reduce the entry barriers associated with quantitative xylem anatomy expanding the use of this technique by the scientific community.

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