  Histopathology images contain essential information for medical diagnosis and
prognosis of cancerous disease. Segmentation of glands in histopathology images
is a primary step for analysis and diagnosis of an unhealthy patient. Due to
the widespread application and the great success of deep neural networks in
intelligent medical diagnosis and histopathology, we propose a modified version
of LinkNet for gland segmentation and recognition of malignant cases. We show
that using specific handcrafted features such as invariant local binary pattern
drastically improves the system performance. The experimental results
demonstrate the competency of the proposed system against state-of-the-art
methods. We achieved the best results in testing on section B images of the
Warwick-QU dataset and obtained comparable results on section A images.
