Abstract
A system for the identification of pollen grains in bright-field microscopic images is presented in this work. The system is based on segmentation of raw images and binary classification for 3 types of pollen grain. The segmentation method developed tackles a major difficulty of the problem: the existence of clustered pollen grains in the initial binary images. Two different SVM classification kernels are compared to identify the 3 pollen types. The method presented in this paper is able to provide a good estimate of the number of pollen grains of Olea Europea (relative error of 1.3%) in microscopic images. For the two others pollen types tested (Corylus and Quercus), the results were not as good (relative errors of 14.5% and 20.3%, respectively).

Abstract
This paper addresses the issue of calibration (or normalization) of macroarray (dotblot) images. It proposes 3 parameters for the evaluation of the impact on the recorded markers of under- and over-exposure during the experimental acquisition of dotblot images – volume (V), saturation (S) and apparent radius (R). These parameters were evaluated using 101 dotblot images obtained from 16 different experiments, with 404 control markers in total. A procedure to simulate the changes on markers by increasing and decreasing exposure times is also presented. This can be the basis of a normalization procedure for dot blot images, which would be an important improvement in the current laboratory image acquisition protocol, reducing the subjectivity both at the acquisition level and at the subsequent image analysis stage. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Skin cancer represents a serious public health problem because of its increasing incidence and subsequent mortality. Among skin cancers, malignant melanoma is by far the most deadly form. Because the early detection of melanoma significantly increases the survival rate of the patient, several noninvasive imaging techniques, such as dermoscopy, have been developed to aid the screening process [1]. Dermoscopy involves the use of an optical instrument paired with a powerful lighting system, allowing the examination of skin lesions in a higher magnification. Therefore, dermoscopic images provide a more detailed view of the morphological structures and patterns than normally magnified images of the skin lesions [1, 2]. However, the visual interpretation and examination of dermoscopic images can be a time-consuming task and, as shown by Kittler et al. [3], the diagnosis accuracy of dermoscopy significantly depends on the experience of the dermatologists. Several medical diagnosis procedures have been introduced in order to guide dermatologists and other health care professionals, for example, pattern analysis, the ABCD rule, the 7-point checklist, and the Menzies method. A number of dermoscopic criteria (i.e., asymmetry, border, colors, differential structures) have to be assessed in these methods to produce the final clinical diagnosis. However, the diagnosis of skin lesions is still a challenging task, even using these medical procedures, mainly due to the subjectivity of clinical interpretation and lack of reproducibility [1, 2]. © 2016 by Taylor and Francis Group, LLC.

Abstract
The automatic analysis of water-sensitive papers (WSP) is of great relevance in agriculture. SprayImageMobile is a software tool developed for mobile devices (iOS) that provides full processing of WSP, from image acquisition to the final reporting. One of the initial processing tasks on SprayImageMobile is the detection (or segmentation) of the WSP on the image acquired by the device. This paper presents the method developed for the detection of the WSP that was implemented in SprayImageMobile. The method is based on the identification of reference points along the WSP margins, and the modeling of a quadrilateral that takes into account possible false positive and negative identifications. The method was tested on a set of 360 images, failing to detect the WSP in only 1 case (detection accuracy of 99.7%). The segmentation accuracy was evaluated using references obtained by a semi-automatic method. The average values obtained for the 359 images tested were: 0.9980 (precision), 0.9940 (recall) and 0.9921 (Hammoude metric). © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Chaos Game Representation (CGR) of DNA sequences has been used for visual representation as well as alignment-free comparisons. CGR is considered to be of great value as the images obtained from parts of a genome present the same structure as those obtained for the whole genome. However, the robustness of the CGR method to compare DNA sequences obtained in a variety of scenarios is not yet fully demonstrated. This paper addresses this issue by presenting a method to evaluate the potential of CGR to distinguish various classes in a DNA dataset. Two indices are proposed for this purpose - a rejection rate and an overlapping rate. The method was applied to 4 datasets, with between 31 to 400 classes each. Nearly 430 million pairs of DNA sequences were compared using the CGR. © 2018, Springer Nature Switzerland AG.

Abstract
An Automatic Calibration of Fertilizers (ACFert) system was developed, for use with centrifugal, pendulum or other types of broadcast spreaders which distribute dry granular agricultural materials on the top of the soil. The ACfert is based on image processing techniques and includes a specially designed mat, which should be placed in the ground for spreaders calibration. A set of images acquired outdoor by a standard device (simple camera) is used to extract information about the spreader distribution pattern. Each image is processed independently, providing as output two numerical values for each grid element present in the image - the number of fertilizers/seeds counted, and its numerical label. The performance of ACFert was evaluated for automatic granules detection using a set of manual counting measurements of nitrate fertilizer and wheat seeds. A total of 185 images acquired with two mobiles devices were used with a total of 498 quadrilateral elements observed and analysed. The overall mean absolute relative error between counting and computed by the ACFert system, were 0.75 +/- 0.75% for fertilizer and 2.12 +/- 1.68% for wheat. This near real-time calibration tool is a very low cost system that can be easily used on field, providing results to support accurate spreader calibration in near real time for different types of fertilizers or seeds.