A method for automatically identifying tubule glandular regions and non-tubule glandular regions in a stained breast tissue sample, the method comprising: (a) detecting nuclei candidates and lumen candidates; (b) applying a random forest classifier to classify each nucleus candidate as a tumor nucleus or a non-tumor nucleus and to classify each lumen candidate as a true lumen or a non-lumina region; (c) identifying glandular regions in an image of the breast tissue sample by grouping each tumor nucleus with neighboring tumor nuclei and with neighboring lumina by a graph-cut method, comprising: building a nuclei-lumen-graph for the image comprising vertices and edges, wherein each vertex represents a tumor nucleus or a true lumen, and wherein each edge represents a link between two tumor nuclei or a link between a tumor nucleus and a true lumen, wherein the nuclei-lumen-graph for the image is built by: (cla) creating a link between two tumor nuclei if the distance between the two nuclei is less than a threshold d n, wherein the threshold d n is estimated by manually observing the usual distance between 2 neighboring nuclei in a gland; (clb) creating a link between a true lumen and a tumor nucleus if the distance from the tumor nucleus to the closest pixel on a boundary of the true lumen boundary is less than a threshold d l, wherein the threshold d l is estimated by manually observing the usual distance between the nuclei and the lumen of the same gland; and (clc) a fixed weight is assigned to all edges; and applying a normalized cut method to partition the graph into different connected components; and (d) classifying the glandular regions identified in (c) as either tubule or non-tubule, wherein a glandular region containing a true lumen is classified as tubule, and wherein a glandular region that does not contain a true lumen is classified as non-tubule. (a) detecting nuclei candidates and lumen candidates; (b) applying a random forest classifier to classify each nucleus candidate as a tumor nucleus or a non-tumor nucleus and to classify each lumen candidate as a true lumen or a non-lumina region; (c) identifying glandular regions in an image of the breast tissue sample by grouping each tumor nucleus with neighboring tumor nuclei and with neighboring lumina by a graph-cut method, comprising: building a nuclei-lumen-graph for the image comprising vertices and edges, wherein each vertex represents a tumor nucleus or a true lumen, and wherein each edge represents a link between two tumor nuclei or a link between a tumor nucleus and a true lumen, wherein the nuclei-lumen-graph for the image is built by: (cla) creating a link between two tumor nuclei if the distance between the two nuclei is less than a threshold d n, wherein the threshold d n is estimated by manually observing the usual distance between 2 neighboring nuclei in a gland; (clb) creating a link between a true lumen and a tumor nucleus if the distance from the tumor nucleus to the closest pixel on a boundary of the true lumen boundary is less than a threshold d l, wherein the threshold d l is estimated by manually observing the usual distance between the nuclei and the lumen of the same gland; and (clc) a fixed weight is assigned to all edges; and applying a normalized cut method to partition the graph into different connected components; and building a nuclei-lumen-graph for the image comprising vertices and edges, wherein each vertex represents a tumor nucleus or a true lumen, and wherein each edge represents a link between two tumor nuclei or a link between a tumor nucleus and a true lumen, wherein the nuclei-lumen-graph for the image is built by: (cla) creating a link between two tumor nuclei if the distance between the two nuclei is less than a threshold d n, wherein the threshold d n is estimated by manually observing the usual distance between 2 neighboring nuclei in a gland; (clb) creating a link between a true lumen and a tumor nucleus if the distance from the tumor nucleus to the closest pixel on a boundary of the true lumen boundary is less than a threshold d l, wherein the threshold d l is estimated by manually observing the usual distance between the nuclei and the lumen of the same gland; and (clc) a fixed weight is assigned to all edges; and (cla) creating a link between two tumor nuclei if the distance between the two nuclei is less than a threshold d n, wherein the threshold d n is estimated by manually observing the usual distance between 2 neighboring nuclei in a gland; (clb) creating a link between a true lumen and a tumor nucleus if the distance from the tumor nucleus to the closest pixel on a boundary of the true lumen boundary is less than a threshold d l, wherein the threshold d l is estimated by manually observing the usual distance between the nuclei and the lumen of the same gland; and (clc) a fixed weight is assigned to all edges; and applying a normalized cut method to partition the graph into different connected components; and (d) classifying the glandular regions identified in (c) as either tubule or non-tubule, wherein a glandular region containing a true lumen is classified as tubule, and wherein a glandular region that does not contain a true lumen is classified as non-tubule.