MULTITHRESHOLDING IN GRAYSCALE IMAGE USING PEA FINDING APPROACH AND HIERARCHICAL CLUSTER ANALYSIS
AbstractAbstract Image segmentation is typically used to distinguish objects that exist in an image. However, it remains difficult to accommodate favourable thresholding in multimodal image histogram problem with specifically desired number of thresholds. This research proposes a novel approach to find thresholds in multimodal grayscale image histogram. This method consists of histogram smoothing, identification of peak(s) and valley(s), and merging process using hierarchical cluster analysis. Using five images that consisted of grayscale and converted-to-grayscale images. This method yields maximum value of accuracy, precision, and recall of 99.93%, 99.75%, and 99.75% respectively. These results are better than the similar peak finding method in multimodal grayscale image segmentation.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).