Abstract-Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts.
In REF , an approach is proposed to improve the accuracy of cell counting by learning the parameter set of each application to reduce the error during fine-tuned filtering.
Automatic adaptation of filter sequences for cell counting
2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Computer Science