Eimeria is an internal animal parasite that causes serious diseases and animal death, and reduces animal productivities. Eimeria has more than one species for every single genus of animals. An early diagnosis of Eimeria infection is usually achieved by examining fecal microscopy images. As Eimeria oocysts vary in terms of shapes, sizes and textures, they can be detected by measuring differences in their shapes, sizes and textural features. As these differences can be driven by analyzing pixel information in microscopic images, this paper therefore presents pixel-based features rather than using the oocysts morphological characteristics. This approach is then applied for the diagnosis of seven different species of Eimeria in chickens as a case study. The pixel-based features are the mean of pixel values over columns and rows of oocyst image matrices in grey-scaled images. The features have been extracted after detecting the oocyst edges by using Moore-Neighbor Tracing Algorithm. For the classification phase, K-Nearest Neighbor classifier was utilized. For its statistical validation, a 5-fold cross validation was adapted and run for 100 times. This proposed approach has yielded an average accuracy of 82% ± 0.54% This is a promising result that is potentially expected to lead fully automated portable parasite detection system.