###### Abstract  
Recent studies have been evaluating the presence of patterns associated with the occurrence of cancer in different types of tissue present in the individual affected by the disease. In this article, we describe preliminary results for the automatic detection of cancer (Walker 256 tumor) in laboratory animals using preclinical microphotograph images of the subject's liver tissue. In the proposed approach, two different types of descriptors were explored to capture texture properties from the images, and we also evaluated the complementarity between them. The first texture descriptor experimented is the widely known Local Phase Quantization (LPQ), which is a descriptor based on spectral information. The second one is built by the application of a granulometry given by a family of morphological filters. For classification, we have evaluated the algorithms Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Logistic Regression. Experiments carried out on a carefully curated dataset developed by the Enteric Neural Plasticity Laboratory of the State University of Maringa showed that both texture descriptors provide good results in this scenario. The accuracy rates obtained using the SVM classifier were 96.67% for the texture operator based on granulometry and 91.16% for the LPQ operator. The dataset was made available also as a contribution of this work. In addition, it is important to remark that the best overall result was obtained by combining classifiers created using both descriptors in a late fusion strategy, achieving an accuracy of 99.16%. The results obtained show that it is possible to automatically perform the identification of cancer in laboratory animals by exploring texture properties found on the tissue taken from the liver. Moreover, we observed a high level of complementarity between the classifiers created using LPQ and granulometry properties in the application addressed here. texture; local phase quantization; granulometry; liver tissue +
Footnote †: journal:  
0000-0001-8899-2000/0000-0001-8999/10/00000-00001-8999/10/00000-00001-8999/10/000000-00001-8999/10/0000000-00001-8999/10/0000000-00001-8999/10/00000000-000001-8999/10/00000000-000001-8999/10/00000000000000000000000 In this work, we describe results obtained in preliminary investigations developed aiming to accomplish the automatic identification of cancer using the aforementioned images. For this purpose, we decided to explore the textural properties of the images, inspired in another biomedical application previously investigated by our research group [2]. In that work, we evaluated the use of some widely known texture operators for the identification of chronic degenerative diseases from images taken from other types of tissue. As far as we know, the automatic identification of cancer, using a spectral texture descriptor and granulometry-based properties of the tissue taken from the liver, is proposed for the first time in this work. Furthermore, we also investigate the complementarity between classifiers created on both scenarios (i.e., the LPQ texture operator [3], and a granulometry-based descriptor [4; 5; 6]). The experimental results demonstrate the existence of a high level of complementarity between both on the task evaluated here. Taking it into account, we describe the following **Research Questions (RQ)** we intend to answer in this work:  
* **RQ1**: What is the performance of LPQ to support cancer identification in a Walker 256 tumor model on microphotograph of rats liver? * **RQ2**: What is the performance of granulometry-based descriptors (GBD) to support cancer identification in a Walker 256 tumor model on microphotographs of rat liver? * **RQ3**: Is it possible to obtain better results for cancer identification in a Walker 256 tumor model by combining classifiers created using LPQ and GBD in this scenario? The classification was performed using three of the most widely known shallow classifiers: k-NN, Logistic Regression, and SVM.