Scale-Adaptive Superpixels

Achanta, Radhakrishna; Neila, Pablo Márquez; Fua, Pascal; Süsstrunk, Sabine

doi:10.2352/issn.2169-2629.2018.26.1

Cited by 21 publications

(26 citation statements)

References 23 publications

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“…color features of each pixel, and in a circular neighborhood were used. Superpixel-wise classification Videos were initially decomposed in superpixels using SLIC [8]. SLIC was applied on each slice of the 3d stack independently.…”

Section: Methodsmentioning

confidence: 99%

“…Considering a cell as a group of pixels, a line drawn between the pixels of the cell contains information on how these pixels are connected [3]. We used this information to find the optimal parameters to decompose an image into superpixels using SLIC [8]. Indeed, an error measure was estimated by counting how many superpixels were crossed by lines of multiple classes ( Figure 5, B).…”

Section: Path-features Separate Cells From Cell Debrismentioning

confidence: 99%

See 1 more Smart Citation

Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

Du¹,

Thelen²,

Sf³

et al. 2019

Preprint

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2-photon intravital microscopy (2P-IVM) is a key technique to investigate cell migration and cell-to-cell interactions in organs and tissues of living organisms. Focusing on immunology, 2P-IVM allowed recording videos of leukocytes during the immune response, highlighting unprecedented mechanisms of the immune system. However, the automatic analysis of the acquired videos remains challenging and poorly reproducible. In fact, both manual curation of results and tuning of bioimaging software parameters among different experiments, are required. One of the most difficult tasks for a user is transferring to a computer the knowledge on what a cell is and how it should appear with respect to the background, other objects, or other cell types. This is possibly due to the low specificity of acquisition channels which may include multiple cell populations and the presence of similar objects in the background. In this work, we propose a method based on semi-supervised machine learning to facilitate colocalization. In line with recently proposed approaches for pixel classification, the method requires the user to draw some lines on the cells of interest and some line on the other objects/background. These lines embed knowledge, not only on which pixel belongs to a class or which pixel belongs to another class but also on how pixels in the same object are connected. Hence, the proposed method exploits the information from the lines to create an additional imaging channel that is specific for the cells fo interest. The usage of this method increased tracking accuracy on a dataset of challenging 2P-IVM videos of leukocytes. Additionally, it allowed processing multiple samples of the same experiment keeping the same mathematical model.

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Section: Methodsmentioning

confidence: 99%

Section: Path-features Separate Cells From Cell Debrismentioning

confidence: 99%

Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

Du¹,

Thelen²,

Sf³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Figure 2 shows that the contrast between tumor and background are enhanced in the processed image, which is convenient for extracting ROI. SLIC [13] is an efficient method to decompose an image in visually homogeneous regions, which is based on the gradient ascent to segment image. Based on the color similarity and spatial distance of pixels, the super-pixel clustering block is obtained by local K-means clustering.…”

Section: Extraction Of Region Of Interestmentioning

confidence: 99%

Breast Cancer Classification with Ultrasound Images Based on SLIC

Fang

Zhang

2020

Lecture Notes in Electrical Engineering

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Ultrasound image diagnosis of breast tumors has been widely used in recent years. However, there are some problems of it, for instance, poor quality, intense noise and uneven echo distribution, which has created a huge obstacle to diagnosis. To overcome these problems, we propose a novel method, a breast cancer classification with ultrasound images based on SLIC (BCCUI). We first utilize the Region of Interest (ROI) extraction based on Simple Linear Iterative Clustering (SLIC) algorithm and region growing algorithm to extract the ROI at the super-pixel level. Next, the features of ROI are extracted. Furthermore, the Support Vector Machine (SVM) classifier is applied. The calculation states that the accuracy of this segment algorithm is up to 88.00% and the sensitivity of the algorithm is up to 92.05%, which proves that the classifier presents in this paper has certain research meaning and applied worthiness.

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“…When comparing state-of-the-art superpixel approaches, SLIC superpixels have outperformed comparable approaches in terms of speed, memory efficiency, compactness and correctness of outlines [24][25][26][27][28][29]. The approach visualized in Figure 4 was introduced in [30] and extended in [24]. SLIC considers image pixels in a 5D space, in terms of their L*a*b values of the CIELAB color space and their x and y coordinates.…”

Section: Line Extraction -Slic Superpixelsmentioning

confidence: 99%

“…Processing pipeline of simple linear iterative clustering (SLIC) resulting in agglomerated groups of pixels, i.e., superpixels, whose boundaries outline objects within the image. The approach is described in [30] and extended in [24].…”

Section: Line Extraction -Slic Superpixelsmentioning

confidence: 99%

Interactive Cadastral Boundary Delineation From Uav Data

Crommelinck

Höfle

Koeva

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a workflow that automatically extracts candidate cadastral boundaries from UAV orthoimages and (ii) a tool for their semi-automatic processing to delineate final cadastral boundaries. The workflow consists of two state-of-the-art computer vision methods, namely gPb contour detection and SLIC superpixels that are transferred to remote sensing in this study. The tool combines the two methods, allows a semi-automatic final delineation and is implemented as a publicly available QGIS plugin. The approach does not yet aim to provide a comparable alternative to manual cadastral mapping procedures. However, the methodological development of the tool towards this goal is developed in this paper. A study with 13 volunteers investigates the design and implementation of the approach and gathers initial qualitative as well as quantitate results. The study revealed points for improvement, which are prioritized based on the study results and which will be addressed in future work.

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Scale-Adaptive Superpixels

Cited by 21 publications

References 23 publications

Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

Semi-supervised machine learning facilitates cell colocalization and tracking in intravital microscopy

Breast Cancer Classification with Ultrasound Images Based on SLIC

Interactive Cadastral Boundary Delineation From Uav Data

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