Review_1: The paper introduces a segmentation procedure for breast tissue images using SVM.  General comments:  a) Concerning the proposed approach: The segmentation method introduced only considers local color/gray value information. Perhaps introducing other features related to the membrane's local (e.g. the membrane has symmetry properties that can be exploited) or global (e.g. continuity, approximately circular shape) properties may improve the segmentation results.  Feature selection is also a key component to improve performance. Use a feature selection procedure.  b) Concerning the experimentation: You should describe the experiments with sufficient detail so others can replicate them: a) Make your data publicly available b) Explain how you have selected the classifier's parameters (parameter selection in an SVM is critical and may improve notably the performance).  You should join Figs 10 and 9 in a single plot so we can better see the differences.  You only provide qualitative segmentation results (Figs. 12, 14, 15), but quantitative results are also very important. Specially when it comes to comparisons. You should quantitatively compare your algorithm with other competing works in the literature.  c) Concerning paper organization: The paper is well written and clearly introduces the problem, describes the relevant literature and presents the solution. I have a few comments concerning the paper organization: a) Problem statement and literature review (intro and theoretical framework sections) are in my opinion a bit long, compared to other important sections, e.g. feature extraction and experimentation which are just one paragraph each. b) Although English is in general correct, there are some small mistakes that could be solved by having it read by a professional. c) There are two "proposed method" sections  Well written paper. Good literature review.  It only shows very preliminary results. Review_2: Este trabajo muestra un enfoque de segmentación de imágenes de tejido mamario para la clasificación binaria de pixels de una imagen mediante SVM, para determinar la presencia o ausencia de la tinción de las membranas de las células cancerígenas Este trabajo es interesante y valorable, debido a la dificultad de la obtención de este tipo de imágenes y por el enfoque interdisciplinario, presentando además un interesante trabajo futuro. Se encuentra bien escrito y los resultados mostrados aunque parciales parecen correctos y justificados.  Verificar redacción y tipografía de la página 4 en sus párrafos primero y penúltimo. La principal debilidad de este trabajo puede ser la baja cantidad de imágenes de prueba y la falta de comparación con otros métodos. También sería deseable mostrar resultados generales de la clasificación y no sólo del proceso de validación. Además, las propiedades explicadas en  la tabla de puntaje (completitud e intensidad) no son utilizadas directamente en el proceso de clasificación. Review_3: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification.  The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written.  - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough.  - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets. 
Output: accept