Modified two-dimensional Otsu image segmentation algorithm and fast realisation IET Image Process., 6 (4) (2012), pp. 426-433 CrossrefView in ScopusGoogle Scholar [52] N.R. Pal, S.K. Pal A review on image segmentation techniques Pattern Recogn., 26 (9) (1993), pp. 1277-1294 View ...
This New Otsu method gives three parts of the image i.e. foreground, background and TBD (To Be Determined) based on the threshold value. The iteration of segmentation is done on the third part i.e., TBD region of the image. TBD region again goes for segmentation and new threshold ...
of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image se...
Robust lane detection based on gradient-pairs constraint J. Duan et al. Lane line recognition algorithm based on threshold segmentation and continuity of lane line Y. Chai et al. The multi-scale hough transform lane detection method based on the algorithm of otsu and cannyView more references ...
Threshold segmentation algorithms (such as the Otsu algorithm, the Niblack algorithm, etc.) are used to divide an image into two parts, foreground and background, to better segment the gestures [33]. The Otsu algorithm is a global threshold segmentation algorithm; its basic idea is to divide ...
The necessary features must be retrieved and selected manually or automatically from the preprocessed picture dataset to train the model using any particular machine learning algorithm [99]. It is possible to do prediction or classification using a trained model [100]. It is a conventional approach...
After obtaining the cell edge probability images, a series of postprocessing steps were conducted to produce the final segmentation images [12]. Briefly, the probability images were binarized using the adaptive Otsu algorithm and cell boundaries at the image borders were then discarded. Morphological ...
Tiles with an occupancy value of less than 0.1, determined by the Otsu algorithm, were discarded to focus on tissue-covered regions. We performed these operations on a cluster of up to 200 nodes, where each node was equipped with 32 CPU cores and 256 GB RAM, completing preprocessing in ...
Finally, the algorithm takes a long time to converge on a large sample set. Machine learning approaches like random forest (RF), and support vector machine (SVM) also apply to image segmentation [9]. But they are being replaced by booming intelligent methods, such as convolutional neural ...
(SBERT and SimCSE) are proposed for detecting cancer in tumor/normal pairs of colorectal cancer patients. In this new approach, the classification algorithm relies on raw DNA sequences as the only input source. Moreover, this work provides a review of the most recent developments in cancers of...